Exploiting Social Networks in Recommendation: a Multi-Domain Comparison
Alejandro Bellogín, Iván Cantador, Pablo Castells, Fernando Díez · 2013
Recommender Systems aim at automatically nding the most useful products or services for a particular user, providing a personalised list of items according to dierent input and attributes of users and items. State-of-the-art recommender systems are usually based on ratings and implicit feedback given by users about the items. Recently, due to the large number of social systems appearing in the so called Web 2.0, where friendship relations between people are explicit, social contexts exploitation has started to receive signicant interest. In particular, social recommenders have started to be investigated that exploit social links between users in a community to suggest interesting items. In this paper we compare a series of experiments developed in recent years with dierent datasets where standard collaborative and social ltering techniques were analysed. We show that social ltering techniques achieve very high performance in the three domains discussed (bookmarks, music, and movies), although they may have lower coverage than traditional collaborative ltering algorithms.