knowledge about current affairs. In this paper, we focus on analyzing social streams in real-time for personalized topic recommendation and discovery. We consider collaborative filtering as an online ranking problem and present Stream Ranking Matrix Factorization – RMFX –, which uses a pairwise approach

demographic aspects such as age and gender. In this paper we propose to merge CFS based on Matrix Factorization and information regarding social friendships in order to provide users with more accurate suggestions and rankings on items of their interest. The proposed approach has been evaluated on a real

Abstract—Content-based social tagging recommendation, which considers the relationship between the tags and the descriptions contained in resources, is proposed to remedy the cold-start problem of collaborative filtering. There is such a common phenomenon that certain tag does not appear

-fective in the rating prediction task, they do not generally provide good results in the top-N recommendation task. In this research, we return to the roots of recommender sys-tems and we explore the relationship between Information Filtering and Information Retrieval. We think that proba-bilistic methods taken from

Trust prediction, which explores the unobserved relationships between online community users, is an emerging and important research topic in social network analysis and many web applications. Similar to other social-based recommender systems, trust relationships between users can be also modeled

Collaborative filtering attempts to find items of interest for a user by utilizing the preferences of other users. In this paper we describe an approach to filtering that explicitly uses social relationships, such as friendship, to find items of interest to a user. Modeling user-item relations as a

focused on improving top-k recommendations. To solve the problem we propose a new CF approach, Collaborative Less-is-More Filtering (CLiMF). In CLiMF the model parameters are learned by directly maximizing the Mean Reciprocal Rank (MRR), which is a well-known information retrieval metric for capturing

, the connection between authority measurement and influence modelling is not clearly established. To this end, in this paper, we provide a focused study on understanding of PageRank as well as the relationship between PageRank and social influence analysis. Along this line, we first propose a linear social

ranked list of potential helpers based on both technical and social measures. A proof of concept experiment shows that the ERSF can recommend experts with good to excellent accuracy, when compared with human rankings of appropriate experts in the same scenarios 1

, this relationship is hidden in the collaboration records and characterized by the development of each researcher and the change of their social role. To discover this potential information from the collaboration data, we need develop a technique to analyze the network evolving with time. This project aims to model