A semantic pattern-based recommender

This paper presents a novel approach for Linked Data-based recommender systems through the use of semantic patterns - generalized paths in a graph described through the types of the nodes and links involved. We apply this novel approach to the book dataset from the ESWC2014 recommender systems challenge. User profiles are built by aggregating ratings on patterns with respect to each book in provided user training set. Ratings are aggregated by estimating the expected value of a Beta distribution describing the rating given to each individual book. Our approach allows the determination of a rating for a book, even if the book is poorly connected with user profile. It allows for a “prudent” estimation thanks to smoothing. However, if many patterns are available, it considers all the contributions. Additionally, it allows for a lightweight computation of ratings as it exploits the knowledge encoded in the patterns. Our approach achieved a precision of 0.60 and an overall F-measure of about 0.52 on the ESWC2014 challenge.