Collaborative filtering is a widely used technique for providing personalized
recommendations to users. However, traditional collaborative filtering methods fail to …

Memory-based approaches for collaborative filtering identify the similarity between two
users by comparing their ratings on a set of items. In the past, the memory-based approach …

This paper is concerned with how to make efficient use of social information to improve
recommendations. Most existing social recommender systems assume people share similar …

Recommender systems use the concept of data analysis and knowledge discovery to
essentially provide personalized recommendations on the web. A number of different …

Predicting people other people may like has recently become an important task in many
online social networks. Traditional collaborative filtering approaches are popular in …

The task of collaborative filtering is to predict the preferences of an active user for unseen
items given preferences of other users. These preferences are typically expressed as …

Several collaborative filtering (CF) approaches have been developed in order to improve the
quality of the recommendations. However, this improvement has always been measured as …

Collaborative filtering is one of widely used recommendation approaches to make
recommendation services for users. The core of this approach is to improve capability for …

Collaborative filtering is probably the most familiar and most widely implemented
recommendation algorithm. However, traditional collaborative filtering methods focus only …

Similarity measures play an important role in the accuracy of collaborative filtering based
recommendation. Due to non-availability of adequate co-rated users, the accuracy of …