Recommendation and Reputation Systems

LIA has been active in this topic for many years, including work on preference-based search and truthful reputation systems. See here for an overview of earlier research. We are currently exploring how to aggregate ratings and rankings obtained from multiple sources. We are also developing systems for generating history-dependent recommendations, in particular for news.

Another project aims at personalizing the rankings constructed by review sites. By analyzing review texts, we discover the subjective sentiment expressed about different facets of a product, thus allowing to personalize the ranking according to the relative importance of the different facets.