Toward Social-Semantic Recommender Systems

Abstract

In this article, the authors consider the basic problem of recommender systems that is identifying a set of users to whom a given item is to be recommended. In practice recommender systems are run against huge sets of users, and the problem is then to avoid scanning the whole user set in order to produce the recommendation list. To cope with problem, they consider that users are connected through a social network and that taxonomy over the items has been defined. These two kinds of information are respectively called social and semantic information. In their contribution the authors suggest combining social information with semantic information in one algorithm in order to compute recommendation lists by visiting a limited part of the social network. In their experiments, the authors use two real data sets, namely Amazon.com and MovieLens, and they compare their algorithms with the standard item-based collaborative filtering and hybrid recommendation algorithms. The results show satisfying accuracy values and a very significant improvement of performance, by exploring a small part of the graph instead of exploring the whole graph.