From Community Detection to Mentor Selection in Rating-Free Collaborative Filtering

—The number of resources or items that users can now access when navigating on the Web or using e-services, is so huge that these might feel lost due to the presence of too much information. Recommender systems are a way to cope with this profusion of data by suggesting items that fit the users’ needs. One of the most popular techniques for recommender systems is the collaborative filtering approach that does not use any a priori information about the users, nor any data about the content of the items. Collaborative filtering relies on the preferences of items expressed by users. These are usually recorded under the form of ratings and the recommendation technique exploits these ratings. However, in many e-services, it is inappropriate to ask to rate items; it may indeed interrupt users’ activity. In the absence of ratings, classical collaborative filtering techniques cannot be applied; especially the selection of like-minded users for a given user, also called his mentor use...

• Type: Journal
• Year: 2011
• Where: AMM
• Authors: Armelle Brun, Sylvain Castagnos, Anne Boyer
• Keywords: Algorithm, AMM 2011, Collaborative, Community Detection