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Information
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Modern Recommender Systems: Approaches, Challenges and Applications
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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) | Viewed by 45654

Special Issue Editors

Prof. Dr. Costas Vassilakis

E-Mail Website
Guest Editor
Department of Informatics and Telecommunications, University of the Peloponnese, Akadimaikou G.K. Vlachou, 22100 Tripoli, Greece
Interests: information systems; recommender systems; semantic web technologies and applications; cultural informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Dionisis Margaris

E-Mail Website
Guest Editor
Department of Informatics and Telecommunications, University of Athens, 15772 Athens, Greece
Interests: personalization; recommender systems; social networks; web services; business processes
Special Issues, Collections and Topics in MDPI journals

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

 

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Published Papers (8 papers)

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Editorial

Jump to: Research, Review

2 pages, 143 KiB  
Editorial
Editorial for the Special Issue on “Modern Recommender Systems: Approaches, Challenges and Applications”
by Costas Vassilakis and Dionisis Margaris
Information 2019, 10(7), 230; https://doi.org/10.3390/info10070230 - 4 Jul 2019
Cited by 1 | Viewed by 3136
Abstract
Recommender systems are nowadays an indispensable part of most personalized systems implementing information access and content delivery, supporting a great variety of user activities [...] Full article
(This article belongs to the Special Issue Modern Recommender Systems: Approaches, Challenges and Applications)

Research

Jump to: Editorial, Review

16 pages, 3322 KiB  
Article
Using an Exponential Random Graph Model to Recommend Academic Collaborators
by Hailah Al-Ballaa, Hmood Al-Dossari and Azeddine Chikh
Information 2019, 10(6), 220; https://doi.org/10.3390/info10060220 - 25 Jun 2019
Cited by 6 | Viewed by 5314
Abstract
Academic collaboration networks can be formed by grouping different faculty members into a single group. Grouping these faculty members together is a complex process that involves searching multiple web pages in order to collect and analyze information, and establishing new connections among prospective [...] Read more.
Academic collaboration networks can be formed by grouping different faculty members into a single group. Grouping these faculty members together is a complex process that involves searching multiple web pages in order to collect and analyze information, and establishing new connections among prospective collaborators. A recommender system (RS) for academic collaborations can help reduce the time and effort required to establish a new collaboration. Content-based recommendation system make recommendations based on similarity without taking social context into consideration. Hybrid recommender systems can be used to combine similarity and social context. In this paper, we propose a weighting method that can be used to combine two or more social context factors in a recommendation engine that leverages an exponential random graph model (ERGM) based on historical network data. We demonstrate our approach using real data from collaborations with faculty members at the College of Computer and Information Sciences (CCIS) in Saudi Arabia. Our results demonstrate that weighting social context factors helps increase recommendation accuracy for new users. Full article
(This article belongs to the Special Issue Modern Recommender Systems: Approaches, Challenges and Applications)
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22 pages, 473 KiB  
Article
Sequeval: An Offline Evaluation Framework for Sequence-Based Recommender Systems
by Diego Monti, Enrico Palumbo, Giuseppe Rizzo and Maurizio Morisio
Information 2019, 10(5), 174; https://doi.org/10.3390/info10050174 - 10 May 2019
Cited by 7 | Viewed by 6515
Abstract
Recommender systems have gained a lot of popularity due to their large adoption in various industries such as entertainment and tourism. Numerous research efforts have focused on formulating and advancing state-of-the-art of systems that recommend the right set of items to the right [...] Read more.
Recommender systems have gained a lot of popularity due to their large adoption in various industries such as entertainment and tourism. Numerous research efforts have focused on formulating and advancing state-of-the-art of systems that recommend the right set of items to the right person. However, these recommender systems are hard to compare since the published evaluation results are computed on diverse datasets and obtained using different methodologies. In this paper, we researched and prototyped an offline evaluation framework called Sequeval that is designed to evaluate recommender systems capable of suggesting sequences of items. We provide a mathematical definition of such sequence-based recommenders, a methodology for performing their evaluation, and the implementation details of eight metrics. We report the lessons learned using this framework for assessing the performance of four baselines and two recommender systems based on Conditional Random Fields (CRF) and Recurrent Neural Networks (RNN), considering two different datasets. Sequeval is publicly available and it aims to become a focal point for researchers and practitioners when experimenting with sequence-based recommender systems, providing comparable and objective evaluation results. Full article
(This article belongs to the Special Issue Modern Recommender Systems: Approaches, Challenges and Applications)
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17 pages, 373 KiB  
Article
Optimizing Parallel Collaborative Filtering Approaches for Improving Recommendation Systems Performance
by Christos Sardianos, Grigorios Ballas Papadatos and Iraklis Varlamis
Information 2019, 10(5), 155; https://doi.org/10.3390/info10050155 - 26 Apr 2019
Cited by 19 | Viewed by 5796
Abstract
Recommender systems are one of the fields of information filtering systems that have attracted great research interest during the past several decades and have been utilized in a large variety of applications, from commercial e-shops to social networks and product review sites. Since [...] Read more.
Recommender systems are one of the fields of information filtering systems that have attracted great research interest during the past several decades and have been utilized in a large variety of applications, from commercial e-shops to social networks and product review sites. Since the applicability of these applications is constantly increasing, the size of the graphs that represent their users and support their functionality increases too. Over the last several years, different approaches have been proposed to deal with the problem of scalability of recommender systems’ algorithms, especially of the group of Collaborative Filtering (CF) algorithms. This article studies the problem of CF algorithms’ parallelization under the prism of graph sparsity, and proposes solutions that may improve the prediction performance of parallel implementations without strongly affecting their time efficiency. We evaluated the proposed approach on a bipartite product-rating network using an implementation on Apache Spark. Full article
(This article belongs to the Special Issue Modern Recommender Systems: Approaches, Challenges and Applications)
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18 pages, 7116 KiB  
Article
Combined Recommendation Algorithm Based on Improved Similarity and Forgetting Curve
by Taoying Li, Linlin Jin, Zebin Wu and Yan Chen
Information 2019, 10(4), 130; https://doi.org/10.3390/info10040130 - 8 Apr 2019
Cited by 24 | Viewed by 6393
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)
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14 pages, 409 KiB  
Article
Mobile Phone Recommender System Using Information Retrieval Technology by Integrating Fuzzy OWA and Gray Relational Analysis
by Shen-Tsu Wang and Meng-Hua Li
Information 2018, 9(12), 326; https://doi.org/10.3390/info9120326 - 14 Dec 2018
Cited by 5 | Viewed by 4093
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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13 pages, 1638 KiB  
Article
Improving Collaborative Filtering-Based Image Recommendation through Use of Eye Gaze Tracking
by Ernani Viriato Melo
Information 2018, 9(11), 262; https://doi.org/10.3390/info9110262 - 23 Oct 2018
Cited by 5 | Viewed by 3939
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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45 pages, 727 KiB  
Review
Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review
by Camila Vaccari Sundermann, Marcos Aurélio Domingues, Roberta Akemi Sinoara, Ricardo Marcondes Marcacini and Solange Oliveira Rezende
Information 2019, 10(2), 42; https://doi.org/10.3390/info10020042 - 28 Jan 2019
Cited by 24 | Viewed by 8057
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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