A collaborative user-centered framework for recommending items in Online Social Networks

Recommender Systems are more and more playing an important role in our life, representing useful tools helping users to find ‘‘what they need’’ from a very large number of candidates and supporting people in making decisions in various contexts: what items to buy, which movie to watch, or even who they can invite to their social network, etc. In this paper, we propose a novel collaborative user-centered recommendation approach in which several aspects related to users and available in Online Social Networks – i.e. preferences (usually in the shape of items’ metadata), opinions (textual comments to which it is possible to associate a sentiment), behavior (in the majority of cases logs of past items’ observations made by users), feedbacks (usually expressed in the form of ratings) – are considered and integrated together with items’ features and context information within a general framework that can support different applications using proper customizations (e.g., recommendation of news, photos, movies, travels, etc.). Experiments on system accuracy and user satisfaction in several domains shows how our approach provides very promising and interesting results.