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Informatics
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Advances in Recommender Systems
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Advances in Recommender Systems

A special issue of Informatics (ISSN 2227-9709).

Deadline for manuscript submissions: closed (31 March 2018) | Viewed by 35112

Special Issue Editor

Prof. Dr. Fernando Ortega

E-Mail Website
Guest Editor
U-tad Centro Universitario de Tecnología y Arte Digital, C/ Playa de Liencres, 2bis. Parque Europa Empresarial. Edificio Madrid, 28029 Las Rozas, Madrid
Interests: recommender systems; machine learning; artificial intelligence

Special Issue Information

Dear Colleagues,

The importance of recommender systems (RS) has grown exponentially with the advent of social networks and the Internet of things. RS are the main solution to the information overload problem. RS acts as filters between users and items allowing the passage of relevant items to the user and blocking the irrelevant ones. Nowadays, RS make use of different sources of information for providing users with predictions and recommendations. They try to balance factors like accuracy, novelty, diversity, serendipity, trust and stability in recommendations.

RS can be classified according to the type of information that they use to compute recommendations. Most popular classifications divides RS into collaborative filtering (CF) and content-based filtering (CBF). CF predicts unknown users’ ratings using clusters of users and items built with the ratings of the users to the items. CBF compares users’ preferences with content information about the items (such as description, tags or location) in order to compute recommendations.

We solicit original submissions that improve RS on any of the following topics:

  • collaborative filtering: Similarity metrics, quality measures, matrix factorization, cold start
  • content based filtering: Topic modeling, folksonomies, geo-based recommendations
  • social media data: Recommendations to group of users, followers, time-based recommendations
  • deep learning in recommender systems
Prof. Dr. Fernando Ortega
Guest Editor

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 submissions that pass pre-check are 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. Informatics is an international peer-reviewed open access quarterly 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 1800 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
  • Collaborative filtering
  • Content based filtering
  • Matrix factorization
  • Deep learning

Published Papers (4 papers)

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Research

19 pages, 915 KiB  
Article
Exploiting Past Users’ Interests and Predictions in an Active Learning Method for Dealing with Cold Start in Recommender Systems
by Manuel Pozo, Raja Chiky, Farid Meziane and Elisabeth Métais
Informatics 2018, 5(3), 35; https://doi.org/10.3390/informatics5030035 - 15 Aug 2018
Cited by 3 | Viewed by 7074
Abstract
This paper focuses on the new users cold-start issue in the context of recommender systems. New users who do not receive pertinent recommendations may abandon the system. In order to cope with this issue, we use active learning techniques. These methods engage the [...] Read more.
This paper focuses on the new users cold-start issue in the context of recommender systems. New users who do not receive pertinent recommendations may abandon the system. In order to cope with this issue, we use active learning techniques. These methods engage the new users to interact with the system by presenting them with a questionnaire that aims to understand their preferences to the related items. In this paper, we propose an active learning technique that exploits past users’ interests and past users’ predictions in order to identify the best questions to ask. Our technique achieves a better performance in terms of precision (RMSE), which leads to learn the users’ preferences in less questions. The experimentations were carried out in a small and public dataset to prove the applicability for handling cold start issues. Full article
(This article belongs to the Special Issue Advances in Recommender Systems)
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16 pages, 537 KiB  
Article
Artificial Neural Networks and Particle Swarm Optimization Algorithms for Preference Prediction in Multi-Criteria Recommender Systems
by Mohamed Hamada and Mohammed Hassan
Informatics 2018, 5(2), 25; https://doi.org/10.3390/informatics5020025 - 9 May 2018
Cited by 29 | Viewed by 9216
Abstract
Recommender systems are powerful online tools that help to overcome problems of information overload. They make personalized recommendations to online users using various data mining and filtering techniques. However, most of the existing recommender systems use a single rating to represent the preference [...] Read more.
Recommender systems are powerful online tools that help to overcome problems of information overload. They make personalized recommendations to online users using various data mining and filtering techniques. However, most of the existing recommender systems use a single rating to represent the preference of user on an item. These techniques have several limitations as the preference of the user towards items may depend on several attributes of the items. Multi-criteria recommender systems extend the single rating recommendation techniques to incorporate multiple criteria ratings for improving recommendation accuracy. However, modeling the criteria ratings in multi-criteria recommender systems to determine the overall preferences of users has been considered as one of the major challenges in multi-criteria recommender systems. In other words, how to additionally take the multi-criteria rating information into account during the recommendation process is one of the problems of multi-criteria recommender systems. This article presents a methodological framework that trains artificial neural networks with particle swarm optimization algorithms and uses the neural networks for integrating the multi-criteria rating information and determining the preferences of users. The proposed neural network-based multi-criteria recommender system is integrated with k-nearest neighborhood collaborative filtering for predicting unknown criteria ratings. The proposed approach has been tested with a multi-criteria dataset for recommending movies to users. The empirical results of the study show that the proposed model has a higher prediction accuracy than the corresponding traditional recommendation technique and other multi-criteria recommender systems. Full article
(This article belongs to the Special Issue Advances in Recommender Systems)
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20 pages, 909 KiB  
Article
Exploiting Rating Abstention Intervals for Addressing Concept Drift in Social Network Recommender Systems
by Dionisis Margaris and Costas Vassilakis
Informatics 2018, 5(2), 21; https://doi.org/10.3390/informatics5020021 - 26 Apr 2018
Cited by 22 | Viewed by 8144
Abstract
One of the major problems that social networks face is the continuous production of successful, user-targeted information in the form of recommendations, which are produced exploiting technology from the field of recommender systems. Recommender systems are based on information about users’ past behavior [...] Read more.
One of the major problems that social networks face is the continuous production of successful, user-targeted information in the form of recommendations, which are produced exploiting technology from the field of recommender systems. Recommender systems are based on information about users’ past behavior to formulate recommendations about their future actions. However, as time goes by, social network users may change preferences and likings: they may like different types of clothes, listen to different singers or even different genres of music and so on. This phenomenon has been termed as concept drift. In this paper: (1) we establish that when a social network user abstains from rating submission for a long time, it is a strong indication that concept drift has occurred and (2) we present a technique that exploits the abstention interval concept, to drop from the database ratings that do not reflect the current social network user’s interests, thus improving prediction quality. Full article
(This article belongs to the Special Issue Advances in Recommender Systems)
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17 pages, 886 KiB  
Article
A Recommender System for Programming Online Judges Using Fuzzy Information Modeling
by Raciel Yera Toledo, Yailé Caballero Mota and Luis Martínez
Informatics 2018, 5(2), 17; https://doi.org/10.3390/informatics5020017 - 3 Apr 2018
Cited by 18 | Viewed by 9522
Abstract
Programming online judges (POJs) are an emerging application scenario in e-learning recommendation areas. Specifically, they are e-learning tools usually used in programming practices for the automatic evaluation of source code developed by students when they are solving programming problems. Usually, they contain a [...] Read more.
Programming online judges (POJs) are an emerging application scenario in e-learning recommendation areas. Specifically, they are e-learning tools usually used in programming practices for the automatic evaluation of source code developed by students when they are solving programming problems. Usually, they contain a large collection of such problems, to be solved by students at their own personalized pace. The more problems in the POJ the harder the selection of the right problem to solve according to previous users performance, causing information overload and a widespread discouragement. This paper presents a recommendation framework to mitigate this issue by suggesting problems to solve in programming online judges, through the use of fuzzy tools which manage the uncertainty related to this scenario. The evaluation of the proposal uses real data obtained from a programming online judge, and shows that the new approach improves previous recommendation strategies which do not consider uncertainty management in the programming online judge scenarios. Specifically, the best results were obtained for short recommendation lists. Full article
(This article belongs to the Special Issue Advances in Recommender Systems)
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