Personalized Recommendation

Machine Learning (ML) is already part of everyday life, powering applications such as Netflix recommendations, Google Maps navigation, and online fraud detection. This article surveys ten practical, real-world use cases of ML, explaining both the technical models and workflows behind them. Case studies include Netflix’s recommendation engine, Google Assistant’s speech models, and Tesla’s autonomous driving systems. By combining technical insights with practical examples, this study highlights how ML is shaping modern society while pointing to future directions such as emotional AI and real-time translation.both researchers and practitioners.

This paper explores how machine learning (ML) is transforming software development by enhancing functionality, decision-making, and performance across diverse industries. It details methodologies for integrating ML models into software systems, including model selection, training, optimization, and real-time deployment. Case studies in fraud detection and embedded applications highlight challenges, optimization strategies, and the importance of continuous learning in maintaining model accuracy. With advancements such as machine learning operations (MLOps) and federated learning, ML is becoming more scalable, transparent, and accessible, even in resource-constrained environments.

Keywords: Machine Learning, Artificial Intelligence, MLOps, Federated Learning, Explainable AI, Predictive Analytics, Fraud Detection, Edge Computing, Software Engineering.
DOI: https://doi.org/10.58812/wsnt.v3i02.1964

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights  A method for cross-context interpretations of health and wellness recommendations.  A mechanism of refining generalized recommendations to personalized recommendations.  The contextual interpretations are made for increasing the user acceptability of a system.

Extracting interest profiles of users based on their personal documents is one of the key topics of IR research. However, when these extracted profiles are used in expert finding applications, only naive text-matching techniques are used to rank experts for a given requirement. In this paper, we address this gap and describe multiple techniques to match user profiles for better ranking of experts. We propose new metrics for computing semantic similarity of user profiles using spreading activation networks derived from ontologies. Our pilot evaluation shows that matching algorithms based on bipartite graphs over semantic user profiles provide the best results. We show that using these techniques, we can find an expert more accurately than other approaches, in particular within the top ranked results. In applications where a group of candidate users need to be short-listed (say, for a job interview), we get very good precision and recall as well.

As the jobs market becomes more competitive, more students are trying to seek admission to overseas institutions for higher education. In the design of a personalized recommendation, identifying the appropriate selection criteria and providing a structured way to prioritizing them is crucial. The objective of this paper is to use the Fuzzy Analytic Hierarchy Process to facilitate the recommendation process. In the past, rarely have the survey questions been deliberately phrased to minimize the possible vagueness in the questions. This paper proposes an enhanced way of phrasing the questions such that the respondents would find the questions more easily understandable. It is argued that better understanding of the questions will minimize the ambiguity inherent in the questions, which would then enhance the consistency and reliability of the answers.

As the jobs market becomes more competitive, more students are trying to seek admission to overseas institutions for higher education. In the design of a personalized recommendation, identifying the appropriate selection criteria and providing a structured way to prioritizing them is crucial. The objective of this paper is to use the Fuzzy Analytic Hierarchy Process to facilitate the recommendation process. In the past, rarely have the survey questions been deliberately phrased to minimize the possible vagueness in the questions. This paper proposes an enhanced way of phrasing the questions such that the respondents would find the questions more easily understandable. It is argued that better understanding of the questions will minimize the ambiguity inherent in the questions, which would then enhance the consistency and reliability of the answers.

With the rapid growth of social tagging systems, many research efforts are being put into personalized search and recommendation using social tags (i.e., folksonomies). As users can freely choose their own vocabulary, social tags can be very ambiguous (for instance, due to the use of homonyms or synonyms). Machine learning techniques (such as clustering and deep neural networks) are usually applied to overcome this tag ambiguity problem. However, the machine-learning-based solutions always need very powerful computing facilities to train recommendation models from a large amount of data, so they are inappropriate to be used in lightweight recommender systems. In this paper, we propose an ontological similarity to tackle the tag ambiguity problem without the need of model training by using contextual information. The novelty of this ontological similarity is that it first leverages external domain ontologies to disambiguate tag information, and then semantically quantifies the relevance between user and item profiles according to the semantic similarity of the matching concepts of tags in the respective profiles. Our experiments show that the proposed ontological similarity is semantically more accurate than the state-of-the-art similarity metrics, and can thus be applied to improve the performance of content-based tag-aware personalized recommendation on the social web. Consequently, as a model-training-free solution, ontological similarity is a good disambiguation choice for lightweight recommender systems and a complement to machine-learningbased recommendation solutions. INDEX TERMS Folksonomies, ontological similarity, personalized recommendation, social tags.

In this paper, some new components that have been integrated in the Diet4You system for the generation of nutritional plans are introduced. Negative user preferences have been modelled and introduced in the system. Furthermore, the cultural eating styles originated from the location where the user lives have been taken into account dividing the original menu plan in sub-plans. Each subplan is in charge to optimize one of the meals of one day in the personal menu of the user. The main latent reasoning mechanism used is case-based reasoning, which reuses previous menu configurations according to the nutritional plan and the corresponding hard constraints and the user preferences to meet a personalized recommendation menu for a given user. It uses the cognitive analogical reasoning technique in addition to ontologies, nutritional databases and expert knowledge. The preliminary results with some examples of application to test the new contextual components have been very satisfactory according to the evaluation of the experts.

Users of ambient intelligence environments have been overwhelmed by the huge numbers of social media available, thus identifying the social media tailored to the user's need is becoming an important question to be discussed. This paper adapts the Katz proximity measure, for the use in social tagging system, to help users in ambient environment find relevant media suited to their interests. The method models the ternary relations among user, resource and tag as a weighted, undirected tripartite graph, then apply the Katz proximity measure to tripartite graph. Experiments on two real datasets are implemented and compared with many state-of-the-art algorithms. The experimental results prove that the adaptation of the Katz algorithm with the tripartite structure yields a significant improvement, and successfully ranks relevant search results according to the user's interests.

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights  A method for cross-context interpretations of health and wellness recommendations.  A mechanism of refining generalized recommendations to personalized recommendations.  The contextual interpretations are made for increasing the user acceptability of a system.

One of the most important steps in building a recommender system is the interaction design process, which defines how the recommender system interacts with a user. It also shapes the experience the user gets, from the point she registers and provides her preferences to the system, to the point she receives recommendations generated by the system. A proper interaction design may improve user experience and hence may result in higher usability of the system, as well as, in higher satisfaction. In this paper, we focus on the interaction design of a mobile food recommender system that, through a novel interaction process, elicits users’ long-term and short-term preferences for recipes. User’s long-term preferences are captured by asking the user to rate and tag familiar recipes, while for collecting the short-term preferences, the user is asked to select the ingredients she would like to include in the recipe to be prepared. Based on the combined exploitation of both types of preference...

Evaluation for ranking is very useful for users in their decisionmaking process when they want to select some item(s) from a large number of items using their personal preferences. In this paper, we will focus on the evaluation of Japanese traditional crafts, in which product items are assessed according to the so-called Kansei features by means of the semantic differential method. In particular, two decision analysis based evaluation procedures, which take consumer-specified preferences on kansei features of traditional products into consideration, will be discussed and compared.

A recommendation system helps an organization to create loyal customers and build trust by offering their desired products and services. These systems today are so powerful that they can handle the new customer too who has visited the site for the first time. With the increasing number of books, people prefer to use e-books. Today, online businesses have emerged that are dedicated only for e-books. They allow their users to purchase any books of their interest or even read them online. This improves their business targets. To make their users engaged, they use machine learning models that recommend users the books based on their preferences. Such a system is called Book Recommendation System. Over the past, a large number of book recommendation systems have been built, most of them are found to be useful for both the organization and the users, and are being put into use in the real world. In this proposed system, we build a Book Recommendation System which recommends a set of books to users based on their previous ratings and readings using content-based filtering, collaborative filtering, and hybrid filtering model. This will save users time in searching the books of their interest.

The rapid growth of personal opinions published in form of microposts, such as those found on Twitter, is the basis of novel emerging social and commercial services. In this paper, we describe BOTTARI, an augmented reality application that permits the personalized and localized recommendation of points of interest (POIs) based on the temporally-weighted opinions of the community. The technological basis of BOTTARI is the highly scalable LarKC platform for the rapid prototyping and development of Semantic Web applications. In particular, BOTTARI exploits LarKC's deductive and inductive stream reasoning. We present an evaluation of BOTTARI based on a three year collection of tweets about 319 restaurants located in the 2 km 2 district of Insadong, a popular tourist area of the South Korean city of Seoul. BOTTARI is the winner of the 9th edition of the Semantic Web Challenge, co-located with the 2011 International Semantic Web Conference. BOTTARI is currently field tested in Korea by Saltlux.

Personalized trip planning is a very common problem in tourism domain. There are several studies in this area each one of all aims to provide recommendations based on user preferences. Recommendation engines mostly use two common methods: content based filtering and collaborative filtering. As a combination of these two methods, hybrid approaches are also popular for recommendation systems. This study provides a deep analysis about recent studies in trip recommendation domain. Applied techniques and mentioned methodologies in literature is discussed at all points. Insights about the proposed systems are provided clearly. Besides a literature survey, this study also proposes a novel travel recommender method based on a tourism datasource. A hybrid approach involving demographic, content-based and collaborative filtering techniques are proposed in order to eliminate drawbacks of each approach. Recommendations will be based on many factors including users' demographic information, past travel locations and favorite seasons. Based on such inputs, recommender engine predicts possible travel locations along with various flight options. Possible challenges and future trends are concluded as a result of this study.