A Fuzzy Relational Approach to Event Recommendation

Most existing recommender systems employ collaborative filtering (CF) techniques in making projections about which items an eservice user is likely to be interested in, i.e. they identify correlations between users and recommend items which similar users have liked in the past. Traditional CF techniques, however, have difficulties when confronted with sparse rating data, and cannot cope at all with time-specific items, like events, which typically receive their ratings only after they have finished. Content-based (CB) algorithms, which consider the internal structure of items and recommend items similar to those a user liked in the past can partly make up for that drawback, but the collaborative feature is totally lost on them. In this paper, modelling user and item similarities as fuzzy relations, which allow to flexibly reflect the graded/uncertain information in the domain, we develop a novel, hybrid CF-CB approach whose rationale is concisely summed up as “recommending future items if they are similar to past ones that similar users have liked”, and which surpasses related work in the same spirit.