Semantic Pattern-based Recommender
نویسندگان
چکیده
This paper presents a novel approach for Linked Data-based recommender systems by means of semantic patterns. We associate to each pattern the rating of the arrival book (0 or 1) and compute user profiles by aggregating, for each book in the user training set, the ratings of all the patterns pointing to that book. Ratings are aggregated by estimating the expected value of a Beta distribution describing the rating given to the 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, obtained by using the Beta distribution. If many patterns are available, it considers all the contributions. Nevertheless, it allows for a lightweight computation of ratings as it exploits the knowledge encoded in the patterns. Without any setup of the system, this approach allowed us to reach a precision of 0.60 and an overall F-measure of about 0.52.
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