Recommender system, Collaborative filtering

Collaborative filtering systems rely heavily on matrix factorization techniques, which often face scalability issues when handling large datasets. This paper presents an efficient parallel algorithm that leverages distributed computing to perform largescale matrix factorization. Experimental results show that our algorithm significantly reduces computation time while maintaining high accuracy. The approach has practical implications for recommendation systems, particularly in ecommerce and social media platforms.

This paper is to present an overview of Collaborative Filtering (CF) recommender system and show the major CF challenges. In general, the recommendation systems are the best way to help users to overcome the information overload issue. The CF approach is one of the most widely used and most successful methods in the recommendation system, such as e-commerce. This paper introduced a brief description about recommender's approaches which are: content-Based, collaborative filtering and hybrid approach. Next, defined the main challenges which have clearly impact on the performance and accuracy of CF recommender system. The major finding of this paper is the CF main problems: Data sparsity, Cold-star, and Scalability. By presenting of these challenges the quality of recommendations can be improved by proposing new methods. The paper ends with conclusion summarizes the limitations of the existing methods and recommendations.

This paper introduces the use of WordNet as a resource for RDF web resources sense disambiguation in Web of Data and shows the role of designed system in interlinking datasets in Web of Data and word sense disambiguation scope. We specify the core labelling properties in semantic web to identify the name of entities which are described in web resources and use them to identify the candidate senses for a web resource. Moreover, we define the web resource's context to identify the most appropriate sense for each of the input web resources. Evaluation of the system shows the high coverage of the core labelling properties and the high performance of the sense disambiguation algorithm.

Recommender System is a subclass of information filtering system. It identifies similarity among users or items. It can be used as information filtering tool in online social network. Collaborative filtering recommendations are based on similarity of users or items, all data should be compared with each other in order to calculate this similarity. Due to large amount of data in dataset, too much time is required for this calculation, and in these systems, scalability problem is observed. It is better to group data, and each data should be compared with data in the same group. Content based filtering recommends items to users according to users history and items he liked in the past. To improve the working and quality of recommender system, a Hybrid approach by combining content based filtering and collaborative filtering, which includes Memory based (K-Nearest Neighbor) and Model based (clustering and rule based techniques),is presented in our proposed methodology. Using Hybrid approach, we get advantages from each other while drawbacks of both methods won't take into account.

Library management are of huge importance in colleges and universities. Though the present management approaches have made major achievements, these approaches do not consider college students' parallel education trajectories in the same way. In order to manage and recommend books to students is very critical for college. This paper proposes a library management and book recommendation process for student and staff on the basis of algorithm which is based on the time sequential collaborative filtering recommendation, which is combined with students' learning path. In order to suggest books resourcefully, our algorithm leverages space distance. In this algorithm, we consider two important characteristics: first is time sequence information of borrowing books and the flow times of books. Our new results show that our book recommendation algorithm is in accordance with the college students' requirement for learning.

There are different types of information systems, such as those that perform group recommendations and market segmentations, which operate with groups of users. In order to combine the individual preferences and properly address suggestions to users, group modeling strategies are employed. Nowadays, data is characterized by large amounts in terms of volume, speed, and variety (the so-called big data issue). In this paper, we are going to tackle the problem of modeling group preferences in big data scenarios. This study will present the existing strategies, and we are going to present criteria to design the algorithms that implement them when big amounts of data have to be combined. Moreover, a set of best practices discusses under which conditions the presented strategies can be adopted in big data scenarios.

Esse artigo a apresenta o resultado de uma revisão bibliográfica sobre explicação de recomendação com diversificação. Constatou-se com base nela que nenhuma pesquisa propôs ainda estudar como gerar explicação de recomendação com diversificação. Foram encontrados apenas trabalhos que indicam a necessidade de haver explicação de recomendação com diversificação. A partir dessa constatação de necessidade de pesquisa propõese, como trabalho futuro, investigar e desenvolver uma abordagem de explicação de recomendação com diversificação. Essa abordagem terá que gerar explicações que sejam interpretáveis e persuasivas de modelos

The aim of recommender system is to provide services and product to the user to improve the customer-relationship management. Researchers recognize that recommendation is a great challenge in the field of Business, education, government and other domains. So it is essential that high quality, review of current trends, not only in the theoretical research result but also in practical developments should be conducted in recommender systems. This paper summarizes the related recommendation

Selecting the most appropriate mining method to recover mineral resources is a critical decision-making task in mining project development. This study introduces an artificial intelligence-based mining methods recommendation system (AI-MMRS) for the pre-selection of underground mining methods. The study integrates and evaluates the capability of two approaches for mining methods selection (MMS): the memory-based collaborative filtering (CF) approach aided by the UBC-MMS system to predict the top-3 relevant mining methods and supervised machine learning (ML) classification algorithms to enhance the effectiveness and novelty of the AI-MMRS, addressing the limitations of the CF approach. The results reveal that the memory-based CF approach achieves an accuracy ranging from 81.8% to 87.9%. Among the classification algorithms, artificial neural network (ANN) and k-nearest neighbors (KNN) classifiers perform the best, with accuracy levels of 66.7% and 63.6%, respectively. These findings demonstrate the effectiveness and viability of both approaches in MMS, acknowledging their limitations and the need for continuous training and optimization. The proposed AI-MMRS for the pre-selection stage supplemented by the direct involvement of mining professionals in later stages of MMS, has the potential to significantly aid in the MMS decision-making, providing data-driven and experience-based recommendations following the ongoing evolution of mining practices.

In MAS studies on Trust building and dynamics the role of direct/personal experience and of recommendations and reputation is proportionally overrated; while the importance of inferential processes in deriving the evaluation of trustees’ trustworthiness is underestimated and not enough exploited. In this paper we focus on the importance of generalized knowledge: agents' categories. The cognitive advantage of generalized knowledge can be synthesized in this claim: "It allows us to know a lot about something/somebody we do not directly know". At a social level this means that I can know a lot of things on people that I never met; it is social "prejudice" with its good side and fundamental contribution to social exchange. In this study we experimentally inquire the role played by categories' reputation with respect to the reputation and opinion on single agents: when it is better to rely on the first ones and when are more reliable the second ones. Our claim...

In this work we demonstrate realization of semantic web by publishing profile of student on web on linked data principles. We also suggest various vocabularies that can be used for publishing profiles of a technical student in an Indian university. We take profiles of final year students at DAIICT, Gandhinagar as a case study. We have used Sesame server for publishing and querying the case data.

Te explosive growth in the amount of available digital information in higher education has created a potential challenge of information overload, which hampers timely access to items of interest. Te recommender systems are applied in diferent domains such as recommendations flm, tourist advising, webpages, news, songs, and products. But the recommender systems pay less attention to university library services. Te most users of university library are students. Tese users have a lack of ability to search and select the appropriate materials from the large repository that meet for their needs. A lot of work has been done on recommender system, but there are technical gaps observed in existing works such as the problem of constant item list in using web usage mining, decision tree induction, and association rule mining. Besides, it is observed that there is cold start problem in case-based reasoning approach. Terefore, this research work presents matrix factorization collaborative fltering with some performance enhancement to overcome cold start problem. In addition, it presents a comparative study among memory-based and model-based approaches. In this study, researchers used design science research method. Te study dataset, 5189 records and 76,888 ratings, was collected from the University of Gondar student information system and online catalogue system. To develop the proposed model, memory-based and model-based approaches have been tested. In memory-based approach, matrix factorization collaborative fltering with some performance enhancements has been implemented. In model-based approach, Knearest neighbour (KNN) and singular value decomposition (SVD) algorithms are also assessed experimentally. Te SVD model is trained on our dataset optimized with a scored RMSE 0.1623 compared to RMSE 0.1991 before the optimization. Te RMSE for a KNN model trained using the same dataset was 1.0535. Tis indicates that the matrix factorization performs better than KNN models in building collaborative fltering recommenders. Te proposed SVD-based model accuracy score is 85%. Te accuracy score of KNN model is 53%. So, the comparative study indicates that matrix factorization technique, specifcally SVD algorithm, outperforms over neighbourhood-based recommenders. Moreover, using hyperparameter tuning with SVD also has an improvement on model performance compared with the existing SVD algorithm.

Nowadays, the social networks are spreading abroad different application domains. Also, the digital libraries are improving how their users exploit the catalog services with social capabilities. More recently, the Linked Data model defines a novel vision for the near future web applications. Our paper approaches a solution for managing users in a digital library with a semantic fashion.

The typical goal of a collaborative filtering algorithm is the minimisation of the deviation between rating predictions and factual user ratings so that the recommender system offers suggestions for appropriate items, achieving a higher prediction value. The datasets on which collaborative filtering algorithms are applied vary in terms of sparsity, i.e., regarding the percentage of empty cells in the user–item rating matrices. Sparsity is an important factor affecting rating prediction accuracy, since research has proven that collaborative filtering over sparse datasets exhibits a lower accuracy. The present work aims to explore, in a broader context, the factors related to rating prediction accuracy in sparse collaborative filtering datasets, indicating that recommending the items that simply achieve higher prediction values than others, without considering other factors, in some cases, can reduce recommendation accuracy and negatively affect the recommender system’s success. An ex...

Travel recommendation agents have been a helpful tool for travelers in their decision-making for destination choices. It has been shown that sparsity can significantly impact on the accuracy of recommendation agents. The COVID-19 outbreak has affected the tourism and hospitality industry of almost all countries in the world. Tourists who have planned to travel are canceling or postponing trips due to this pandemic. Accordingly, this will impact the rate of travelers’ online reviews on tourism products. Hence, the lack of data, in terms of ratings and textual reviews on hotels, will be a major issue for travel recommendation agents during the COVID-19 outbreak in the context of tourism and hospitality. This will be a new challenge for the researchers in the development of travel recommendation agents. Machine learning has been found to be effective in dealing with the data sparsity in recommendation agents. Therefore, developing new algorithms would be helpful to overcome the sparsit...

Empresas que fazem distribuição de produtos no atacado normalmente têm grande quantidade de itens comercializados de diversos fabricantes em seu portfólio de produtos. Assim, encontrar e recomendar produtos ao cliente entre milhares de possibilidades se caracteriza num desafio para a equipe de vendas. O objetivo deste artigo é mostrar uma arquitetura de sistema de recomendação para implantação em ferramentas de sistema de automação de força de vendas e suportado por Inteligência Artificial que possa recomendar produtos online a cada interação no processo de venda. Foram usados os seguintes os métodos e materiais: revisão sistemática da literatura, pesquisa exploratória quali-quantitativa, experimentos computacionais, estudo de caso, prova de conceito do sistema e pesquisa com o método Delphi junto a especialistas com aplicação de questionários. A arquitetura foi aplicada construindo-se uma prova de conceito numa empresa de distribuição de autopeças na qual o sistema foi capaz de for...

In MAS studies on Trust building and dynamics the role of direct/personal experience and of recommendations and reputation is proportionally overrated; while the importance of inferential processes in deriving the evaluation of trustees' trustworthiness is underestimated and not enough exploited. In this paper we focus on the importance of generalized knowledge: agents' categories. The cognitive advantage of generalized knowledge can be synthesized in this claim: "It allows us to know a lot about something/somebody we do not directly know". At a social level this means that I can know a lot of things on people that I never met; it is social "prejudice" with its good side and fundamental contribution to social exchange. In this study we experimentally inquire the role played by categories' reputation with respect to the reputation and opinion on single agents: when it is better to rely on the first ones and when are more reliable the second ones. Our claim is that: the larger the population and the ignorance about the trustworthiness of each individual (as it happens in an open world) the more precious the role of trust in categories. In particular, we want investigate how the parameters defining the specific environment (number of agents, their interactions, transfer of reputation, and so on) determine the use of categories' reputation. This powerful inferential device has to be strongly present in WEB societies.

This paper describes a process to develop and publish a scorecard from an OAJ (Open Access Journal) on the Semantic Web using Linked Data technologies in such a way that it can be linked to related datasets. Furthermore, methodological guidelines are presented with activities related to each step of the process. The proposed process was applied to a university OAJ, including the definition of the KPIs (Key Performance Indicators) linked to the institutional strategies, the extraction, cleaning and loading of data from the data sources into a data mart, the transformation of data into RDF (Resource Description Framework), and the publication of data by means of a SPARQL endpoint using the Virtuoso software. Additionally, the RDF data cube vocabulary has been used to publish the multidimensional data on the Web. The visualization was made using CubeViz, a faceted browser to present the KPIs in interactive charts.

Computing k-nearest-neighbor graphs constitutes a fundamental operation in a variety of data-mining applications. As a prominent example, user-based collaborative-filtering provides recommendations by identifying the items appreciated by the closest neighbors of a target user. As this kind of applications evolve, they will require KNN algorithms to operate on more and more sensitive data. This has prompted researchers to propose decentralized peer-to-peer KNN solutions that avoid concentrating all information in the hands of one central organization. Unfortunately, such decentralized solutions remain vulnerable to malicious peers that attempt to collect and exploit information on participating users. In this paper, we seek to overcome this limitation by proposing H&S (Hide & Share), a novel landmark-based similarity mechanism for decentralized KNN computation. Landmarks allow users (and the associated peers) to estimate how close they lay to one another without disclosing their individual profiles. We evaluate H&S in the context of a user-based collaborativefiltering recommender with publicly available traces from existing recommendation systems. We show that although landmarkbased similarity does disturb similarity values (to ensure privacy), the quality of the recommendations is not as significantly hampered. We also show that the mere fact of disturbing similarity values turns out to be an asset because it prevents a malicious user from performing a profile reconstruction attack against other users, thus reinforcing users' privacy. Finally, we provide a formal privacy guarantee by computing an upper bound on the amount of information revealed by H&S about a user's profile.

This paper identifies the factors that have an impact on mobile recommender systems. Recommender systems have become a technology that has been widely used by various online applications in situations where there is an information overload problem. Numerous applications such as e-Commerce, video platforms and social networks provide personalized recommendations to their users and this has improved the user experience and vendor revenues. The development of recommender systems has been focused mostly on the proposal of new algorithms that provide more accurate recommendations. However, the use of mobile devices and the rapid growth of the internet and networking infrastructure has brought the necessity of using mobile recommender systems. The links between web and mobile recommender systems are described along with how the recommendations in mobile environments can be improved. This work is focused on identifying the links between web and mobile recommender systems and to provide solid future directions that aim to lead in a more integrated mobile recommendation domain.

Collaborative filtering (CF) is one of the most widely utilised approaches in recommendation techniques. It suggests items to users based on the ratings of other users who share their preferences. Thus, one of the aims of CF is to find reliable neighbours. Typically, CF produces a sparse user-item rating matrix, when relying only on the ratings to identify the precise neighbours, resulting in poor performance. User reviews can be essential in overcoming those situations because of the diverse elements available in reviews. The most popular element is aspects, which can provide a fine-grained analysis of users' behaviours, thus improving personalised recommendations. However, increasing the number of aspects also results in sparsity, therefore may deteriorate the recommendation performance. As a result, clustering of aspects may lessen this sparsity, but it is yet unclear how much this would affect the performance of CF systems. This study proposes a CF approach based on aspect clustering that addresses the above issue in terms of rating prediction. The approach aims to reduce the sparseness in the multi-criteria rating matrix by grouping aspects into clusters based on their semantic similarity, which will be less expensive and require less memory to discover the neighbourhood set. Our approach extracts aspects and represents them using Google's pretrained Word2vec model. Then, aspects are organised into clusters using the K-means clustering algorithm. Multi-dimensional Euclidean distance is used as a similarity measure for finding the appropriate neighbours and predicted ratings of unseen items are then made using the kNN algorithm. This study also identifies the number of aspects that significantly impacts CF performance. Experiments are carried out using a real large-scale dataset: the Amazon movie dataset. Evaluation is also performed by comparing CF performance of the proposed approach with three different baseline approaches. Results show that the proposed approach improves CF performance compared to other approaches in terms of three predictive accuracy metrics.

The goal of this chapter is to give an overview of recent works on the development of social link-based recommender systems and to offer insights on related issues and the future direction. Among several branches of social recommendations, specifically, this chapter focuses on recommendations, which are founded on users' self-defined (i.e., explicit) social links and target to suggest desirable items, not social links of interests. This chapter provides a brief overview over the technical needs for social link-based recommendations and the studies explaining the viability of users' social networks as useful information sources from a social science point of view. Then, the in-depth understanding of existing recommendations based on users' social links will be addressed. Lastly, various issues and future directions of social link-based recommendation research are put forward.

With the rapid development and application of the mobile Internet, huge amounts of user data are generated and collected every day. How to take full advantages of these ubiquitous data is becoming the essential aspect of a recommender system. Collaborative filtering (CF) has been widely studied and utilized to predict the interests of mobile users and to make proper recommendations. In this paper, we first propose a framework of the CF recommender system based on various user data including user ratings and user behaviors. Key features of these two kinds of data are discussed. Moreover, several typical CF algorithms are classified as memory-based approaches and model-based approaches and compared. Two case studies are presented in an effort to validate the proposed framework. INDEX TERMS Mobile Internet, recommender system, collaborative filtering.

In this paper we introduce our application HappyMovie, a Facebook application for movie recommendation to groups. This system takes advantage of social data available in this social network to promote fairness for the provided recommendations. Group recommendations are based in the individual satisfaction of each individual. The (in)satisfaction of users modifies the typical aggregation functions used to estimate the value of an item for the group. This paper proposes a memory of past recommendations to compute the satisfaction of users when similar items (movies, in this case) are recommended several times.

Library management are of huge importance in colleges and universities. Though the present management approaches have made major achievements, these approaches do not consider college students' parallel education trajectories in the same way. In order to manage and recommend books to students is very critical for college. This paper proposes a library management and book recommendation process for student and staff on the basis of algorithm which is based on the time sequential collaborative filtering recommendation, which is combined with students' learning path. In order to suggest books resourcefully, our algorithm leverages space distance. In this algorithm, we consider two important characteristics: first is time sequence information of borrowing books and the flow times of books. Our new results show that our book recommendation algorithm is in accordance with the college students' requirement for learning.

Recommendation systems have been developed from the web. These recommendation systems are useful in collecting information from an available set of sources for a user's preferences. The information can be acquired from user's collection of details to share, to review, to do positive ratings by monitoring the user's behavior to improve the quality of top 'N' recommendations. Now if we come to modern learning system, it has good framework to influence the training factors from the data, triggers, and learner's preferences. Modern learning can be compared to online learning which carries to the future needs. Modern learning can be instituted in schools, engineering colleges, and working campus. The modern learning system combines interrelated data, processes, and resources to create a system of interdependencies that work together, adapting to changing business needs. These interdependencies include multi-level dynamics driven by the organization, training professionals, technological advances, and the learners themselves.

Recommender systems suggest the most appropriate items to users in order to help customers to find the most relevant items and facilitate sales. Collaborative filtering recommendation algorithm is the most successful technique for recommendation. In view of the fact that collaborative filtering systems depend on neighbors as the source of information, the recommendation quality of this approach depends on the neighbor's selection. However, selecting neighbors can either stem from similarity or trust metrics. In this paper, we analyze these two types of neighbor's selection metrics used in the field of recommendation in the literature. For each type, we first define it and then review different proposed metrics.

When information from traditional recommender systems is augmented with information about user relationships that social networks store, more successful recommendations can be produced. However, this information regarding user relationships may not always be available, since some users may not consent to the use of their social network information for recommendations or may not have social network accounts at all. Moreover, the rating data (categories and characteristics of products) may be unavailable for a recommender system. In this paper, we present an algorithm that can be applied in any social network-aware recommender system that utilizes the users' ratings on items and users' social relations. The proposed algorithm addresses the issues of limited social network information or limited collaborative filtering information for some users by adapting its behavior, taking into account the density and utility of each user's social network and collaborative filtering neighborhoods. Through this adaptation, the proposed algorithm achieves considerable improvement in rating prediction accuracy. Furthermore, the proposed algorithm can be easily implemented in recommender systems.

Agradeço ao meu orientador, Rafael B. Stern, pela paciência com a minha desorganização, pelos bons conselhos e, não o menos importante, pelo apoio moral nos momentos de maior estresse. Agradeço aos professores Rafael Izbicki e Hermes Senger pelas discussões, sugestões e dicas. Agradeço aos meus pais pelo apoio, e à minha avó por perguntar toda semana se já terminei.

Online advertising benefits by recommender systems since the latter analyse reviews and rating of products, providing useful insight of the buyer perception of products and services. When traditional recommender system information is enriched with social network information, more successful recommendations are produced, since more users' aspects are taken into consideration. However, social network information may be unavailable since some users may not have social network accounts or may not consent to their use for recommendations, while rating data may be unavailable due to the cold start phenomenon. In this paper, we propose an algorithm that combines limited collaborative filtering information, comprised only of users' ratings on items, with limited social network information, comprised only of users' social relations, in order to improve (1) prediction accuracy and (2) prediction coverage in collaborative filtering recommender systems, at the same time. The proposed algorithm considerably improves rating prediction accuracy and coverage, while it can be easily integrated in recommender systems.

The rapid proliferation of social network services (SNS) gives people the opportunity to express their thoughts, opinions, and tastes on a wide variety of subjects such as movies or commercial items. Most item shopping websites currently provide SNS systems to collect users' opinions, including rating and text reviews. In this context, user modeling and hyper-personalization of contents reduce information overload and improve both the efficiency of the marketing process and the user's overall satisfaction. As is well known, users' behavior is usually subject to sparsity and their preferences remain hidden in a latent subspace. A majority of recommendation systems focus on ranking the items by describing this subspace appropriately but neglect to properly justify why they should be recommended based on the user's opinion. In this paper, we intend to extract the intrinsic opinion subspace from users' text reviews-by means of collaborative filtering techniquesin order to capture their tastes and predict their future opinions on items not yet reviewed. We will show how users' reviews can be predicted by using a set of words related to their opinions.

Despite challenges like concept drifts, or temporal dynamics in RS, RS has grown in popularity due to its usefulness in meeting customers' needs by helping them find things they might like based on past purchases and interests. Despite their great effectiveness in generating recommendations, conventional RS techniques fall short when it comes to providing accurate ideas due to problems with concept drift. The development of temporal models to account for concept drifts and guarantee more accurate recommendations has been the focus of a lot of research in the wake of these issues, giving rise to dynamic recommender systems (DRSs). However, the bulk of the effort needed to address the drift of interest is put in developing long-term and short-term models for users. It is not possible to dynamically track users' changing interests. We express doubts in this position paper about the practice of computing evaluation metrics for recommender systems as single numbers since these values only represent average effectiveness over a very long time period (for example, a year or longer). This approach only provides a vague, unchanging picture of the facts. To better understand the performance of recommender systems, we propose that researchers compute metrics across time series such as weeks or months and present the results visually, using a line chart, for instance. Insightful forecasts about an algorithm's future performance can be made with the use of results that show how an algorithm's effectiveness evolves over time. As a result, we'll be able to make more informed selections about which algorithms to utilize in a recommender system by collecting more data on an algorithm's performance over time, spotting trends, and developing more accurate forecasts about an algorithm's future performance.

Despite challenges like concept drifts, or temporal dynamics in RS, RS has grown in popularity due to its usefulness in meeting customers' needs by helping them find things they might like based on past purchases and interests. Despite their great effectiveness in generating recommendations, conventional RS techniques fall short when it comes to providing accurate ideas due to problems with concept drift. The development of temporal models to account for concept drifts and guarantee more accurate recommendations has been the focus of a lot of research in the wake of these issues, giving rise to dynamic recommender systems (DRSs). However, the bulk of the effort needed to address the drift of interest is put in developing long-term and short-term models for users. It is not possible to dynamically track users' changing interests. We express doubts in this position paper about the practice of computing evaluation metrics for recommender systems as single numbers since these values only represent average effectiveness over a very long time period (for example, a year or longer). This approach only provides a vague, unchanging picture of the facts. To better understand the performance of recommender systems, we propose that researchers compute metrics across time series such as weeks or months and present the results visually, using a line chart, for instance. Insightful forecasts about an algorithm's future performance can be made with the use of results that show how an algorithm's effectiveness evolves over time. As a result, we'll be able to make more informed selections about which algorithms to utilize in a recommender system by collecting more data on an algorithm's performance over time, spotting trends, and developing more accurate forecasts about an algorithm's future performance.

Communication with the proper information can be helpful for any person to carry out conversations. The proposed system is to help people to interact freely with full information about the past conversations with the person they are meeting. The device UPAL will identify the face and voice of the person and will store necessary details about the meeting, by recording the conversations or by taking inputs from the user. Next time when the user meets the same person, the device will fetch the information from the storage that can be used for a comfortable conversation. UPAL is made up of a Camera and microphone that will use the Face recognition technique and voice recognition system to collect the data. A mobile-based application will be provided to the user for viewing, editing the stored information. UPAL will ensure smart conversation by guiding and reminding the user.

The Open Digital Rights Language (ODRL) is a standard widely adopted to express privacy policies. This article presents several challenges identifed in the context of the European project AURO-RAL in which ODRL is used to express privacy policies for Smart Communities and Rural Areas. The article presents that some challenges should be addressed directly by the ODRL standardisation group to achieve the best course of action, although others exists. For others, the authors have presented a potential solution, in particular, for considering dynamic values coming from external data sources into privacy policies. Finally, the last challenge is an open research question, since it revolves around the interoperability of privacy policies that belong to diferent systems and that are expressed with diferent privacy languages.

Given the increasing growth of the Web and consequently the growth of e-commerce, the amount of data which users face are increasing day by day. Therefore, one of the key issues in today's world is the extraction of knowledge from a large database. The recommendation systems are able to extract useful information from large databases. The information extracted by the recommendation systems may breach the privacy-preserving of individuals and increase the error rate. Concerns will grow along with the increasing privacy breaches, which are done by recommendation systems. In recent years, researchers have provided a variety of techniques for privacy-preserving and reduced error rates in recommendation systems. But most of these methods have not offered good solutions for privacy-preserving issues and reducing error rates. The aim of the proposed method is to provide a solution for users' security concerns in common filtering systems with reduced error rates and more privacy preservation. In this article, we propose a privacy-preserving method for recommendation systems called PRS, which first uses an anonymous method to convert secondary data without user identification information. The existing trust data are measured in terms of resemblance and trust-weighted criterion and then converted from perturbation-based chaos to confidential data. Finally, these two algorithms have been used for clustering the data: fuzzy c-ordered means and particle swarm optimization. The results of experiments have been compared with state-of-the-art methods, which show the superiority of the proposed method in terms of classification error rates and privacy-preserving. Keywords Privacy-preserving Á Error rate Á Collaborative filtering Á Recommendation systems Á Clustering Á Perturbation Á Chaos Communicated by V. Loia.

Semantic and context knowledge have been envisioned as an appropriate solution for addressing the content heterogeneity and information overload in mobile Web information access, but few have explored their full potential in mobile scenarios, where information objects refer to their physical counterparts, and retrieval is context-aware and personalized for users. We present SMARTMUSEUM, a mobile ubiquitous recommender system for the Web of Data, and its application to information needs of tourists in context-aware on-site access to cultural heritage. The SMART-MUSEUM system utilizes Semantic Web languages as the form of data representation. Ontologies are used to bridge the semantic gap between heterogeneous content descriptions, sensor inputs, and user profiles. The system makes use of an information retrieval framework wherein context data and search result clustering are used in recommendation of suitable content for mobile users. Results from laboratory experiments demonstrate that ontology-based reasoning, query expansion, search result clustering, and context knowledge lead to significant improvement in recommendation performance. The results from field trials show that the usability of the system meets users' expectations in real-world use. The results indicate that semantic content representation and retrieval can significantly improve the performance of mobile recommender systems in knowledge-rich domains.

With the increasing number of items in electronic retailers, news websites, etc., finding interesting items concerning the taste of users is becoming more challenging. Recommender Systems (RS) are a well-known solution to this issue. Collaborative filtering (CF) is a widely accepted and popular technique to implement an RS. However, cold-start and data sparsity problems reduce the performance of CF methods. One promising solution for these issues is to use the social trust information. However, how to properly use social trust information is a hot and still open question. In this paper, we propose a similarity measure and a simple link prediction method to address this question and employ them in trustaware matrix factorization. Especially, our proposed similarity measure is asymmetric to consider the nature of social relationships. Also, to have a more accurate similarity estimation, we have considered both the user's historical ratings and trust relations, and we have determined the weight of each source. Finally, we have used the item-based model and the level of interest a user's trustees have for an item to improve the performance of the proposed method for sparse datasets. We conduct extensive performance evaluations in terms of rate prediction and interesting items found. Experimental results on three real-world datasets demonstrate the effectiveness of the proposed method, especially in terms of Mean Absolute Error.

Penelitian ini mengusulkan sebuah klasifikasi terhadap sepeda motor berdasarkan karakteristik konsumen. Sepeda motor memiliki beberapa jenis dan merk yang berbeda sehingga menyebabkan banyaknya pilihan yang dimiliki konsumen. Konsumen akan memilih sepeda motor yang diinginkannya berdasarkan latar belakang yang berbeda. Pada penelitian ini, Konsumen akan dikelompokkan berdasarkan sepeda motor yang dibeli sehingga penjual dapat mengetahui karakteristik konsumen yang membeli suatu jenis atau merk tertentu. Karakterisitik konsumen dapat ditentukan dengan usia, jenis kelamin, pendapatan, status pernikahan dan jumlah anak. Berdasarkan karakteristik tersebut perlu dilakukan pengelompokan untuk menentukan merk sepeda motor. Dalam penelitian ini metode yang digunakan yakni K-Nearest Neighbour (K-NN). K-NN merupakan algoritma yang umum digunakan untuk klasifikasi dan mencari kelas dari data uji dengan mayoritas kelompok yang memiliki jarak terdekat. Dataset yang digunakan dalam penelitian ini yaitu karakteristik konsumen. Uji coba dengan dataset tersebut menghasilkan merk sepeda motor dari data uji yang sudah ditentukan.

Travel recommendation agents have been a helpful tool for travelers in their decision-making for destination choices. It has been shown that sparsity can signi cantly impact on the accuracy of recommendation agents. e COVID-19 outbreak has a ected the tourism and hospitality industry of almost all countries in the world. Tourists who have planned to travel are canceling or postponing trips due to this pandemic. Accordingly, this will impact the rate of travelers' online reviews on tourism products. Hence, the lack of data, in terms of ratings and textual reviews on hotels, will be a major issue for travel recommendation agents during the COVID-19 outbreak in the context of tourism and hospitality. is will be a new challenge for the researchers in the development of travel recommendation agents. Machine learning has been found to be e ective in dealing with the data sparsity in recommendation agents. erefore, developing new algorithms would be helpful to overcome the sparsity issue in travel recommendation agents. is research provides a new method through neurofuzzy, dimensionality reduction, and clustering techniques and evaluates it on the TripAdvisor dataset to see its effectiveness in solving the sparsity issue. e results showed that the method which used the fuzzy logic technique with the aid of clustering, dimensionality reduction, and fuzzy logic is more e cient in addressing the sparsity problem and presenting more accurate results. e results of the method evaluation are presented and discussed, and several suggestions are provided for future studies.

Penelitian ini mengusulkan sebuah klasifikasi terhadap sepeda motor berdasarkan karakteristik konsumen. Sepeda motor memiliki beberapa jenis dan merk yang berbeda sehingga menyebabkan banyaknya pilihan yang dimiliki konsumen. Konsumen akan memilih sepeda motor yang diinginkannya berdasarkan latar belakang yang berbeda. Pada penelitian ini, Konsumen akan dikelompokkan berdasarkan sepeda motor yang dibeli sehingga penjual dapat mengetahui karakteristik konsumen yang membeli suatu jenis atau merk tertentu. Karakterisitik konsumen dapat ditentukan dengan usia, jenis kelamin, pendapatan, status pernikahan dan jumlah anak. Berdasarkan karakteristik tersebut perlu dilakukan pengelompokan untuk menentukan merk sepeda motor. Dalam penelitian ini metode yang digunakan yakni K-Nearest Neighbour (K-NN). K-NN merupakan algoritma yang umum digunakan untuk klasifikasi dan mencari kelas dari data uji dengan mayoritas kelompok yang memiliki jarak terdekat. Dataset yang digunakan dalam penelitian ini yaitu karakteristik konsumen. Uji coba dengan dataset tersebut menghasilkan merk sepeda motor dari data uji yang sudah ditentukan.

Rainfall is a natural factor that is very important for farmers or certain institutions to predict the planting period of a plant. The problem is that rainfall is very difficult to predict. Trials to get optimal rainfall prediction have been carried out by BMKG through research with variety of methods in various fields, including meteorology, climatology and geophysics. The results of the study unfortunately obtained a less optimal success rate in predicting rainfall. Today, there are many new methods for predicting events. These methods include deep learning (DL) and Particle swarm optimization (PSO). The use of the deep learning method is very susceptible to initial weights that are less than optimal, so it requires a process of optimization using a metaheuristic technique, which is the PSO algorithm, because this algorithm has a level of complexity that is much lower than genetic algorithms. In this study, this method is utilized to predict rainfall by determining the exact regression equation model according to the number of layers in hidden nodes based on the size of the kernel and the weight between the layers. This research is approved achieved get more optimal rainfall prediction results that those of previous research that without optimization with PSO.

Recommender systems attempt to suggest information that is of potential interest to users helping them to quickly find information relevant to them. In addition to historical user-item interaction data, such as users' ratings on items, social recommendation methods use social relationships between users to improve the accuracy of recommendations. However, the available social relationships are often extremely sparse. Therefore, incorporating implicit relationships into the recommendation process can be effective to improve the performance of social recommender systems, especially for those users whose explicit relationships are insufficient to make accurate recommendations. The existing approaches have not considered reliability of the implicit relationships. In this paper, a social recommender system is proposed based on reliable implicit relationships. To this end, Dempster-Shafer theory is used as a powerful mathematical tool to calculate the implicit relationships. Moreover, a new measure is introduced to evaluate the reliability of predictions, where unreliable predictions are recalculated using a neighborhood improvement mechanism. This mechanism uses a confidence measure between the users to identify ineffective users in the neighborhood set of a target user. Finally, new reliable ratings are calculated by removing the identified ineffective neighbors. Extensive experiments are conducted on three well-known datasets, and the results demonstrate that our approach achieves superior performance to the state-of-the-art recommendation methods.

Collaborative Filtering (CF) approaches have been widely used in various applications of recommender systems. These methods are based on estimating the similarity between users/items by analyzing the ratings provided by users. The existing methods are often domain-specific and have not considered the time of the ratings being made in the calculation of the similarity. However, users' preferences vary over time, and so their similarity. In this paper, a novel method is proposed by re-ranking the users/items neighborhood set considering their future similarity trend. The trend of similarity is predicted, and depending on increased/decreased trend, we update the final nearest neighbor sets that are used in CF formulation. This method can be applied on a broad range of CF methods that are based on similarities between users and/or items. We apply the proposed approach on a set of CF algorithms over two benchmark datasets and show that the proposed approach significantly improves the performance of the original CF recommenders. As the proposed method only re-ranks the neighborhood set, it can be applied to any existing non-temporal similarity-based CF recommenders to improve their performance.

The main goal of recommender systems is to predict unknown ratings of items for users. This can be seen as the task to complete the user-item matrix. Method such as matrix factorization can solve this task and have been successfully applied in various domains. However, for some scenarios these general approaches work not as well. So there is a need of some mechanism which can use user-item information and user ratings instead of classical two dimensional matrix recommender systems. In this paper, we have incorporated user-item background information using fuzzy logic in such a way that it improves the performance of traditional recommender systems.

This research paper presents a study on developing a machine learning-based system to provide suggestions for music, utilizing a dataset from Asia's leading music streaming service. The purpose is the study to build a better music system for suggestions and provide personalized recommendations for listeners based on their previous listening behavior. The proposed approach employs both content-based as well as collaborative filtering approaches to produce suggestions. The content-based approach analyzes the properties associated with music, such as genre, tempo, and melody, to find similar songs. The collaborative filtering
approach uses user behavior data to recognize other people that have similar hobbies and music preferences and recommends songs that they have listened to. The paper presents the planning and carrying out of the system for a song suggestion, including the data collection, preprocessing,
and feature extraction steps. The system is evaluated using the dataset from the music streaming service and compared to a number of baseline algorithms. The conclusions show if the suggested system exceeds the baseline algorithms in relation to recommendation accuracy and diversity. This paper ends with a discussion of conceivable applications and limitations in terms of the planned music recommendation system, as well
as future directions for investigating this field. In general, the research demonstrates the effectiveness of methods of learning from machines for building better suggestions for song systems which can improve the music experience with hearing for users.