How to Improve Multi-Agent Recommendations Using Data from Social Networks?
Fabiana Lorenzi, Bruno Fontanella, Edson Prestes, André Luis Xavier Peres · The Florida AI Research Society · 2014
User profiles have an important role in multi-agent recommender systems. The information stored in them improves the system’s generated recommendations. Multi-agent recommender systems learn from previous recommendations to update users’ profiles and improve next recommendations according to the user feedback. However, when the user does not evaluate the recommendations the system may deliver poor recommendations in the future. This paper presents a mechanism that explores user information from social networks to update the user profile and to generate implicit evaluations on behalf of the user. The mechanism was validated with travel packages recommendations and some preliminary results illustrate how user information gathered from social networks may help to improve recommendations in multiagent recommender systems.