Special Issue "Modern Recommender Systems: Approaches, Challenges and Applications"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: closed (15 April 2019)

Special Issue Editors

Guest Editor
Prof. Costas Vassilakis

Department of Informatics and Telecommunications, University of the Peloponnese, Greece
Website | E-Mail
Interests: Information systems; Recommender systems; Semantic web technologies and applications; Cultural informatics
Guest Editor
Dr. Dionisis Margaris

Department of Informatics and Telecommunications, University of Athens, Greece
Website | E-Mail
Interests: Recommender systems; Service-oriented systems; Information systems

Special Issue Information

Dear Colleagues,

Recommender systems are nowadays an indispensable part of most personalized systems implementing information access and content delivery, supporting a great variety of user activities. Recommender systems alleviate the problem of information overload, identifying and promoting content that is deemed more suitable for each individual user. To this end, recommender systems collect and process information about user preferences, likings and previous actions; the user’s current context (such as the user’s location or company, the time of day or week, etc.); the user’s neighborhood and activity in social networks (friends, posts, message exchanges and so forth); the characteristics of items to be recommended, including semantic information; and so on. Both static and dynamic views of the collected data are considered, and the algorithms employed to process the available data range from collaborative filtering and statistical models to knowledge-based approaches and matrix factorization.

This Special Issue on “Modern Recommender Systems: Approaches, Challenges and Applications” aims to promote new theoretical models, approaches, algorithms and applications related to the area of recommender systems. Authors should submit papers describing significant, original and unpublished work. Possible topics include but are not limited to:

  • Models and algorithms to improve recommendation quality.
  • Recommendation algorithms that exploit contextual information and/or social network information and/or rich item descriptions.
  • Techniques and methods for enhancing recommender system performance in the context of big data.
  • Privacy preserving techniques for recommender systems.
  • Novel recommender system applications.
  • Case studies of real-world implementations
  • Algorithm scalability, performance, and implementations
  • Cross-disciplinary approaches involving recommender systems

Prof. Costas Vassilakis
Dr. Dionisis Margaris
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 850 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • recommender systems
  • contextual information
  • social networks
  • item semantics
  • big data and performance
  • privacy preservation

 

Published Papers (4 papers)

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Research

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Open AccessArticle Combined Recommendation Algorithm Based on Improved Similarity and Forgetting Curve
Information 2019, 10(4), 130; https://doi.org/10.3390/info10040130
Received: 17 December 2018 / Revised: 8 March 2019 / Accepted: 3 April 2019 / Published: 8 April 2019
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Abstract
The recommendation algorithm in e-commerce systems is faced with the problem of high sparsity of users’ score data and interest’s shift, which greatly affects the performance of recommendation. Hence, a combined recommendation algorithm based on improved similarity and forgetting curve is proposed. Firstly, [...] Read more.
The recommendation algorithm in e-commerce systems is faced with the problem of high sparsity of users’ score data and interest’s shift, which greatly affects the performance of recommendation. Hence, a combined recommendation algorithm based on improved similarity and forgetting curve is proposed. Firstly, the Pearson similarity is improved by a wide range of weighted factors to enhance the quality of Pearson similarity for high sparse data. Secondly, the Ebbinghaus forgetting curve is introduced to track a user’s interest shift. User score is weighted according to the residual memory of forgetting function. Users’ interest changing with time is tracked by scoring, which increases both accuracy of recommendation algorithm and users’ satisfaction. The two algorithms are then combined together. Finally, the MovieLens dataset is employed to evaluate different algorithms and results show that the proposed algorithm decreases mean absolute error (MAE) by 12.2%, average coverage 1.41%, and increases average precision by 10.52%, respectively. Full article
(This article belongs to the Special Issue Modern Recommender Systems: Approaches, Challenges and Applications)
Open AccessArticle Mobile Phone Recommender System Using Information Retrieval Technology by Integrating Fuzzy OWA and Gray Relational Analysis
Information 2018, 9(12), 326; https://doi.org/10.3390/info9120326
Received: 20 November 2018 / Revised: 2 December 2018 / Accepted: 12 December 2018 / Published: 14 December 2018
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Abstract
With the advancement and diversification of information retrieval technology, such technology has been widely applied in recent years in personalized information recommender systems (RSs) and e-commerce RSs in addition to data-mining applications, especially with respect to mobile phone purchases. By integrating the weights [...] Read more.
With the advancement and diversification of information retrieval technology, such technology has been widely applied in recent years in personalized information recommender systems (RSs) and e-commerce RSs in addition to data-mining applications, especially with respect to mobile phone purchases. By integrating the weights of fuzzy ordered weighted averaging (OWA) and gray relational analysis, this research calculated the recommended F1 indices of three weight calculation methods to be 20.5%, 14.36%, and 16.43% after an examination by 30 experimenters. According to the operational results attained by the 30 experimenters, the recommended products obtained by the fuzzy OWA and gray relational analysis calculation method covered the products recommended by the other two weight calculation methods with a higher recommendation effect. Full article
(This article belongs to the Special Issue Modern Recommender Systems: Approaches, Challenges and Applications)
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Open AccessArticle Improving Collaborative Filtering-Based Image Recommendation through Use of Eye Gaze Tracking
Information 2018, 9(11), 262; https://doi.org/10.3390/info9110262
Received: 15 September 2018 / Revised: 10 October 2018 / Accepted: 19 October 2018 / Published: 23 October 2018
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Abstract
Due to the overwhelming variety of products and services currently available on electronic commerce sites, the consumer finds it difficult to encounter products of preference. It is common that product preference be influenced by the visual appearance of the image associated with the [...] Read more.
Due to the overwhelming variety of products and services currently available on electronic commerce sites, the consumer finds it difficult to encounter products of preference. It is common that product preference be influenced by the visual appearance of the image associated with the product. In this context, Recommendation Systems for products that are associated with Images (IRS) become vitally important in aiding consumers to find those products considered as pleasing or useful. In general, these IRS use the Collaborative Filtering technique that is based on the behaviour passed on by users. One of the principal challenges found with this technique is the need for the user to supply information concerning their preference. Therefore, methods for obtaining implicit information are desirable. In this work, the author proposes an investigation to discover to which extent information concerning user visual attention can aid in producing a more precise IRS. This work proposes therefore a new approach, which combines the preferences passed on from the user, by means of ratings and visual attention data. The experimental results show that our approach exceeds that of the state of the art. Full article
(This article belongs to the Special Issue Modern Recommender Systems: Approaches, Challenges and Applications)
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Review

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Open AccessReview Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review
Information 2019, 10(2), 42; https://doi.org/10.3390/info10020042
Received: 14 December 2018 / Revised: 19 January 2019 / Accepted: 22 January 2019 / Published: 28 January 2019
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Abstract
Recommender systems help users by recommending items, such as products and services, that can be of interest to these users. Context-aware recommender systems have been widely investigated in both academia and industry because they can make recommendations based on a user’s current context [...] Read more.
Recommender systems help users by recommending items, such as products and services, that can be of interest to these users. Context-aware recommender systems have been widely investigated in both academia and industry because they can make recommendations based on a user’s current context (e.g., location and time). Moreover, the advent of Web 2.0 and the growing popularity of social and e-commerce media sites have encouraged users to naturally write texts describing their assessment of items. There are increasing efforts to incorporate the rich information embedded in user’s reviews/texts into the recommender systems. Given the importance of this type of texts and their usage along with opinion mining and contextual information extraction techniques for recommender systems, we present a systematic review on the recommender systems that explore both contextual information and opinion mining. This systematic review followed a well-defined protocol. Its results were based on 17 papers, selected among 195 papers identified in four digital libraries. The results of this review give a general summary of the current research on this subject and point out some areas that may be improved in future primary works. Full article
(This article belongs to the Special Issue Modern Recommender Systems: Approaches, Challenges and Applications)
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