Recommendation System Using Deep Learning

A recommender system is an Information Retrieval technology that improves access and proactively recommends relevant items to users by considering the users' explicitly mentioned preferences and objective behaviors. A recommender system is one of the major techniques that handle information overload problem of Information Retrieval by suggesting users with appropriate and relevant items. Today, several recommender systems have been developed for different domains however, these are not precise enough to fulfil the information needs of users. Therefore, it is necessary to build high quality recommender systems. In designing such recommenders, designers face several issues and challenges that need proper attention. This paper investigates and reports the current trends, issues, challenges, and research opportunities in developing high-quality recommender systems. If properly followed, these issues and challenges will introduce new research avenues and the goal towards fine-tuned and high-quality recommender systems can be achieved.

2015

Recommender Systems (RSs) are software tools and techniques that provide suggestions for items that are most likely of interest to a particular user [17, 41, 42]. The suggestions relate to various decision-making processes, such as what items to buy, what music to listen to, or what online news to read. “Item” is the general term used to denote what the system recommends to users. An RS normally focuses on a specific type of item (e.g., CDs or news) and, accordingly its design, its graphical user interface, and the core recommendation technique used to generate the recommendations are all customized to provide useful and effective suggestions for that specific type of item. RSs are primarily directed toward individuals who lack the sufficient personal experience or competence in order to evaluate the potentially overwhelming number of alternative items that a website, for example, may offer [42]. A prime example is a book recommender system that assists users in selecting a book to ...

International Journal of Engineering & Technology

Recommender systems are one of the important methodologies in machine learning technologies, which is using in current business scenario. This article proposes a book recommender system using deep learning technique and k-Nearest Neighbors (k-NN) classification. Deep learning technique is one of the most effective techniques in the field of recommender systems. Recommender systems are intelligent systems in Machine Learning that can make difference from other algorithms. This article considers application of Machine Learning Technology and we present an approach based a recommender system. We used k-Nearest Neighbors classification algorithm of deep learning technique to classify users based book recommender system. We analyze the traditional collaborative filtering with our methodology and also to compare with them. Our outcomes display the projected algorithm is more precise over the existing algorithm, it also consumes less time and reliable than the existing methods.

Proceedings of the 14th Brazilian Symposium on Multimedia and the Web - WebMedia '08, 2008

This paper describes a Recommender System for scientific articles in digital libraries for the Computer Science researchers' community. The system employs the Dublin Core metadata standard for the documents description, the XML standard for describing user profile, which is based on the user's Curriculum, and on service and data providers to generate recommendations. The main contribution of this work is to provide a recommendation mechanism based on the user academic curriculum reducing the human effort spent on the profile generation. In addition, this article presents and discusses some experiments that are based on quantitative and qualitative evaluations.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023

For many applications, particularly in the academic environment and industry, the Recommendation System for Technical Paper Reviewers is very important. This study examines the research trends connecting the highly technical components of recommendation systems employed in various service fields to their commercial aspects. It is a technique that enables the user to identify the information that will be useful to him or her from the variety of facts accessible. In terms of the movie recommendation system, recommendations are made either based on user similarities in collaborative filtering or by considering the user's intended engagement with the content into account content-based filtering. A stronger recommendation system is produced by combining content-based and collaborative filtering, which overcomes the issues that collaborative and content-based filtering typically have. The similarity between users is also determined using a variety of similarity measures in order to make recommendations. We have reviewed cutting-edge approaches to collaborative filtering, content-based filtering, deep learning-based methods, and hybrid approaches in this study for movie recommendation. Additionally, we looked at other similarity measures. Numerous businesses, including Facebook, which suggests friends, LinkedIn, which suggests jobs, Pandora, which suggests music, Netflix, which suggests movies, and Amazon, which suggests purchases, among others, employ recommendation systems to boost their profits and help their clients. This essay primarily focuses on providing a succinct overview of the many approaches and techniques used for movie recommendation in order to investigate the field of recommendation systems research.

2010

At the 2010 annual ACM Conference on Recommender Systems (RecSys 2010) a panel addressed emerging topics regarding recommender systems as a whole and specifically their role in industry. This report summarizes answers from a distinguished group of industry leaders representing different industries in which recommender systems are highly relevant. Panel members discuss questions regarding the role of recommender systems in their own industry area, killer applications, opportunities, and future directions.

Library Philosophy and Practice, 2019

The study hopefully has given an understanding of Recommender System (RS) concept and trends of IR systems especially in the domain of digital libraries. It unfolded the concept of RS through the review of literature and presented an outline of the concepts. Paper discussed the importance of recommender systems in the digital library domain. Study further explain the concept of different kind of RS applied to different digital library software systems. This paper shows how recommender systems functions in different library systems and how these recommender system helps to the users to find and retrieve data or information from different databases. The basic aim of this paper is to know the future aspects of recommender systems in digital library systems and the implications according to its need. This paper contains about conceptual base of the recommender systems, their approaches and their usability in different field of information gathering systems.

Communications on Applied Electronics (CAE), ISSN : 2394-4714, Foundation of Computer Science, New York, USA, 2016

Recommender systems are extensively seen as an effective means to combat information overload, as they redound us both narrow down the number of items to choose. They are seen as assistance us make better decisions at a lower transaction cost. Hence, recommender systems have become omnipresent in e-commerce and are also increasingly used in services in different other domains both online and offline where the number of items exceeds our potentiality to consider them all individually. The research papers recommender systems are software applications or systems that help individual users to discover the most relevant research papers to their needs. These systems use filtering techniques to create recommendations. These techniques are categorized majorly into collaborative-based filtering, content-based technique, and hybrid algorithm. In addition, they assist in decision making by providing product information both personalized and non-personalized, summarizing community opinion, search research papers, and providing community critiques. As a result, recommender systems have been shown to ameliorate the decision.

International Journal of Business and Systems Research, 2021

Recommender system (RS) has emerged as a major research interest that aims to help users to find items online by providing suggestions that closely match their interest. This paper provides a comprehensive study on the RS covering the different recommendation approaches, associated issues, and techniques used for information retrieval. Thanks to its widespread applications, it has induced research interest among a significant number of researchers around the globe. The main purpose of this paper is to spot the research trend in RS. More than 1,000 research papers, published by ACM, IEEE, Springer, and Elsevier since 2011 to the first quarter of 2017, have been considered. Several interesting findings have come out of this study, which will help the current and future RS researchers to assess and set their research roadmap. Furthermore, this paper also envisions the future of RS which may open up new research directions in this domain.

International Journal of Applied Information Systems (IJAIS), ISSN : 2249-0868, Foundation of Computer Science FCS, New York, USA, 2015

In the last twelve years, the number of web user increases, so intensely leading to intense advancement in web services which leads to enlargement the usage data at higher rates. The purpose of a recommender System is to generate meaningful recommendations to a collection of users for items or products that might interest them. Recommender systems differ in the way they analyze these data sources to develop notions of congeniality between users and items which can be used to identify well-matched pairs. The recommender system technology intentions to help users in finding items that match their personal interests. It has a successful usage in e-commerce applications to deal with problems related to information overload proficiently. In this paper, we will extensively present a survey of six existing recommendation system. The Collaborative Filtering systems analyze historical interactions alone, while Content-Based Filtering systems are based on profile attributes, Hybrid Techniques attempt to combine both of these designs, Demographic Based Recommender systems aim to categorize the user based on personal attributes and make recommendations based on demographic classes, while Knowledge-Based Recommendation attempts to suggest objects based on inferences about a user's needs and preferences, and Utility-Based Recommender systems make recommendations based on the computation of the utility of each item for the user. In this paper, we have recognized 60 research papers on recommender systems, which were published between 1971 and 2014. Finally, few research papers had an influence on research paper recommender systems in practice. We also recognized a lack of authority and long term research interest in the field, 78% of the authors published no more than one paper on research paper recommender systems, and there was miniature cooperation among different co-author groups.