Editorial

2 pages, 143 KiB

Open AccessEditorial

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 - 04 Jul 2019

Cited by 1 | Viewed by 2819

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

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16 pages, 3322 KiB

Open AccessArticle

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 5 | Viewed by 4887

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

Open AccessArticle

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 6 | Viewed by 6025

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

Open AccessFeature PaperArticle

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 18 | Viewed by 5105

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

Open AccessArticle

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 - 08 Apr 2019

Cited by 21 | Viewed by 5745

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

Open AccessArticle

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 4 | Viewed by 3668

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

Open AccessArticle

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 4 | Viewed by 3527

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

Open AccessReview

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 18 | Viewed by 7356

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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