Special Issue "Social Networks and Recommender Systems"
Deadline for manuscript submissions: 28 February 2019
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
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.
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- online social networks
- recommender systems
- social network analysis
- big data
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
Author: FLORA AMATO, GIOVANNI COZZOLINO, GIANCARLO SPERLI'
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.