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

-nificantly improves both accuracy and serendipity for top-N recommendation compared to traditional personalized ranking methods in various settings.

For recommender systems that base their product rankings primarily on a measure of similarity between items and the user query, it can often happen that products on the recommendation list are highly similar to each other and lack diversity. In this article we argue that the motivation of diversity

Recommender systems are frequently used in domains in which users express their preferences in the form of graded judgments, such as ratings. If accurate top-N recommenda-tion lists are to be produced for such graded relevance do-mains, it is critical to generate a ranked list of recommended items

Collaborative filtering techniques rely on aggregated user preference data to make personalized predictions. In many cases, users are reluctant to explicitly express their prefer-ences and many recommender systems have to infer them from implicit user behaviors, such as clicking a link in a webpage

into account both the social annotation and friendships inherent in the social graph established among users, items and tags, we created a collaborative recommendation system that effectively adapts to the personal information needs of each user. We adopt the generic framework of Random Walk with Restarts

Studies of information seeking and workplace collaboration often find that social relationships are a strong factor in determining who collaborates with whom. Social networks provide one means of visualizing existing and potential interaction in organizational settings. Groupware designers

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

of user preference; and furthermore, a better view of the task is of generating a top-N list of items that the user is most likely to like. In this regard, we argue that collaborative filtering can be directly cast as a relevance ranking problem. We begin with the classic Probability Ranking Principle

Nowadays social websites have become a major trend in the Web 2.0 environment, enabling abundant social data available. In this paper, we explore the role of two types of social relationships: membership and friendship, while being fused with traditional CF (Collaborative Filtering) recommender