CS Associate Research Scientist John O'Donovan and his associate Barry Smyth have been awarded "Most-Influential-Paper-Award in 2017" for their work titled Trust in Recommender Systems, originally published at IUI 2005. The award will be presented during the 2017 Intelligent User Interface Conference in Limassol, Cyrus.

Abstract
Recommender systems have proven to be an important response to the information overload problem, by providing users with more proactive and personalized information services. And collaborative filtering techniques have proven to be an vital component of many such recommender systems as they facilitate the generation of high-quality recommendations by leveraging the preferences of communities of similar users. In this paper we suggest that the traditional emphasis on user similarity may be overstated. We argue that additional factors have an important role to play in guiding recommendation. Specifically we propose that the trustworthiness of users must be an important consideration. We present two computational models of trust and show how they can be readily incorporated into standard collaborative filtering frameworks in a variety of ways. We also show how these trust models can lead to improved predictive accuracy during recommendation.

 Read the full-text paper here.