Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

Transforming Graph Representations for Statistical Relational Learning

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed

Identifying Multiple Influential Users Based on the Overlapping Influence in Multiplex Networks

Online social networks (OSNs) are interaction platforms that can promote knowledge spreading, rumor propagation, and virus diffusion. Identifying influential users in OSNs is of great significance for accelerating the information propagation especially when information is able to travel across multiple channels. However, most previous studies are limited to a single network or select multiple influential users based on the centrality ranking result of each user, not addressing the overlapping influence (OI) among users. In practice, the collective influence of multiple users is not equal to the total sum of these users' influences. In this paper, we propose a novel OI-based method for identifying multiple influential users in multiplex social networks. We first define the effective spreading shortest path (ESSP) by utilizing the concept of spreading rate in order to denote the relative location of users. Then, the collective influence is quantified by taking the topological factor and the location distribution of users into account. The identified users based on our proposed method are central and relatively scattered with a low overlapping influence. With the Susceptible-Infected-Recovered (SIR) model, we estimate our proposed method with other benchmark algorithms. Experimental results in both synthetic and real-world networks verify that our proposed method has a better performance in terms of the spreading efficiency. © 2013 IEEE

MULTIMEDIA SOCIAL NETWORKS

Nowadays, On-Line Social Networks represent an interactive platform to share -- and very often interact with -- heterogeneous content for different purposes (e.g to comment events and facts, express and share personal opinions on specific topics, and so on), allowing millions of individuals to create on-line profiles and communicate personal information. In this dissertation, we define a novel data model for Multimedia Social Networks (MSNs), i.e. social networks that combine information on users -- belonging to one or more social communities -- with the multimedia content that is generated and used within the related environments. The proposed data model, inspired by hypergraph-based approaches, allows to represent in a simple way all the different kinds of relationships that are typical of these environments (among multimedia contents, among users and multimedia content and among users themselves) and to enable several kinds of analytics and applications. Exploiting the feature of MSN model, the following two main challenging problems have been addressed: the Influence Maximization and the Community Detection. Regarding the first problem, a novel influence diffusion model has been proposed that, learning recurrent user behaviors from past logs, estimates the probability that a given user can influence the other ones, basically exploiting user to content actions. On the top of this model, several algorithms (based on game theory, epidemiological etc.) have been developed to address the Influence Maximization problem. Concerning the second challenge, we propose an algorithm that leverages both user interactions and multimedia content in terms of high and low-level features for identifying communities in heterogeneous network. Finally, experimental analysis have been made on a real Multimedia Social Network ("Flickr") for evaluating both the feasibility of the model and the effectiveness of the proposed approaches for Influence Maximization and community detection