Recommender systems exploit interaction history to estimate user preference, having been
heavily used in a wide range of industry applications. However, static recommendation …

Collaborative filtering (CF) methods produce recommendations based on usage patterns
without the need of exogenous information about items or users. CF algorithms have shown …

“Nature shows us only the tail of the lion. But I do not doubt that the lion belongs to it even
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …

Recommender systems have been in existence everywhere with most of them using single
ratings in prediction. However, multi-criteria predictions have been proved to be more …

Although Recommender Systems have been comprehensively analyzed in the past decade,
the study of social-based recommender systems just started. In this paper, aiming at …

Recently, location-based social networks (LBSNs), such as Gowalla, Foursquare, Facebook,
and Brightkite, etc., have attracted millions of users to share their social friendship and their …

Recommendation plays an increasingly important role in our daily lives. Recommender
systems automatically suggest to a user items that might be of interest to her. Recent studies …

Recommender systems provide users with personalized suggestions for products or
services. These systems often rely on Collaborating Filtering (CF), where past transactions …

Customer preferences for products are drifting over time. Product perception and popularity
are constantly changing as new selection emerges. Similarly, customer inclinations are …

Among collaborative recommendation approaches, methods based on nearest-neighbors
still enjoy a huge amount of popularity, due to their simplicity, their efficiency, and their ability …