Special Issue "Social Networks and Recommender Systems"

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

Deadline for manuscript submissions: 28 February 2019

Special Issue Editors

Guest Editor
Prof. Vincenzo Moscato

Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Napoli, Italy
Website | E-Mail
Interests: multimedia, database systems, knowledge management, big data
Guest Editor
Prof. Antonio Picariello

Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Napoli, Italy
Website | E-Mail
Interests: big data, multimedia data base, image analysis and process

Special Issue Information

Dear Colleagues,

Nowadays, Online Social Networks (OSNs) are becoming the most important medium in which to exchange information, ideas, opinions, and different kinds of content among people: they generate a huge amount of data showing Big Data features, mainly due to their high change rate, large volume, and intrinsic heterogeneity. On the other hand, in the last decade Recommender Systems have been introduced to support the browsing of very large data collections for various applications (e.g., e-commerce, multimedia sharing, cultural heritage, tourism, etc.), assisting users to find “what they really need”. The coupling of OSNs with recommender systems offers new opportunities for researchers. In particular, social network users’ relationships, interactions (with other users or generated content) and properties—through Social Network Analysis (SNA)—can surely improve recommender performances. In such a context, there are still many challenges that have to be faced to realize a new generation of large scale recommender systems that leverage complex information coming from different OSNs to efficiently provide users with better personalized recommendations. This Special Issue on “Social Networks and Recommender Systems” aims to promote new theories, techniques, and methods with which to exploit social data within a recommendation framework. Potential topics include, but not limited to, the following:

  • Social media recommender systems,
  • Large-scale parallel and distributed implementations of social media recommender systems,
  • Applications of social media recommender systems (e.g., cultural heritage, tourism, etc.)
  • Context-aware recommender systems incorporating social information,
  • Novel recommendation techniques for social network applications,
  • Enhancing recommender performances using social big data,
  • Multimedia recommender systems for social networks,
  • Privacy preserving in social recommender systems.

Prof. Vincenzo Moscato
Prof. Antonio Picariello
Guest Editors

Manuscript Submission Information

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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 1000 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.


  • online social networks
  • recommender systems
  • social network analysis
  • big data

Published Papers (1 paper)

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Open AccessArticle A Recommendation Model Based on Multi-Emotion Similarity in the Social Networks
Information 2019, 10(1), 18; https://doi.org/10.3390/info10010018
Received: 29 November 2018 / Revised: 29 December 2018 / Accepted: 1 January 2019 / Published: 6 January 2019
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This paper proposed a recommendation model called RM-SA, which is based on multi-emotional analysis in networks. In the RM-MES scheme, the recommendation values of goods are primarily derived from the probabilities calculated by a similar existing recommendation system during the initiation stage of
[...] Read more.
This paper proposed a recommendation model called RM-SA, which is based on multi-emotional analysis in networks. In the RM-MES scheme, the recommendation values of goods are primarily derived from the probabilities calculated by a similar existing recommendation system during the initiation stage of the recommendation system. First, the behaviors of those users can be divided into three aspects, including browsing goods, buying goods only, and purchasing–evaluating goods. Then, the characteristics of goods and the emotional information of user are considered to determine similarities between users and stores. We chose the most similar shop as the reference existing shop in the experiment. Then, the recommendation probability matrix of both the existing store and the new store is computed based on the similarities between users and target user, who are randomly selected. Finally, we used co-purchasing metadata from Amazon and a certain kind of comments to verify the effectiveness and performance of the RM-MES scheme proposed in this paper through comprehensive experiments. The final results showed that the precision, recall, and F1-measure were increased by 19.07%, 20.73% and 21.02% respectively. Full article
(This article belongs to the Special Issue Social Networks and Recommender Systems)

Figure 1

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Advanced Recommender Systems by Exploiting Social Networks
Author: Mouzhi Ge, Fabio Persia and Daniela D'Auria.
Affiliation: Free University of Bozen-Bolzano, Italy
Abstract: Social networks have become an indispensable part of our lives, which serve as communication channels, social interaction platforms as well as ubiquitous entertainment tools. Meanwhile social networks constantly generate Big Data that create decision complexity and information overload for users. Thus, recommender systems are emerged to suggest personalized and possibly preferred objects for the user in social networks. However, the traditional recommender systems are mostly based on the user-item rating matrix, and the Big Data in social networks have extensively enriched the dimensions of the inputs for recommender systems, for example, social relationships, data source credibility, and new social media types. Therefore, this paper identifies a set of crucial factors that can be used to advance recommender systems in social networks. For each factor, this paper discusses the state-of-the-art recommender system research in this aspect, and suggests how to integrate the featured data to build and improve recommender systems for social networks.

Title: Expert finding in Business Social Media
Affiliation: University of Naples Federico II
Abstract: Online Social Network (OSN) have found widespread applications in each area of our life. A large number of people signed up into OSN for different purposes, as well as to meet old friends, to choose a particular business object, to identify expert user about a given topic, producing a large number of social connections.
This aspect produces information overload that makes complex and time consuming the decision making process, in particular in Business Social Media (such as Yelp, FourSquare, TripAdvisor and so on) where the user has interest to choose a particular product or business object.
Therefore an important research topic concerns the identification of expert user on a given topic. In this paper, we propose a novel data model based on hypergraph datastructure to represent the well-known relationships, that can be classified into the following three groups: user-to-user, user-to-multimedia and multimedia-to-multimedia.
On top of this data model we introduce a topic-user ranking function for identifying expert user in Social Business Media. Finally, several experiments on Yelp Dataset Challenge concerning the effectiveness of the approach have been reported and discussed.  



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