Exploring Semantic Features for Producing Top-N Recommendation Lists from Binary User Feedback

Abstract

In this paper, we report the experiments that we conducted for two of the tasks of the ESWC’14 Challenge on Linked Open Data (LOD)-enabled Recommender Systems. Task 2 and Task 3 dealt with the top-N recommendation problem from a binary user feedback dataset and results were evaluated on the accuracy and diversity respectively of the recommendations produced in a Top-N recommendation list for each user. The DBbook dataset was used in both tracks in which the books had been mapped to their corresponding DBpedia URIs. Since the mappings could be used to extract semantic features from DBpedia, in all our experiments, we avoided the use of any collaborative filtering methods (e.g. user/item K-nearest neighbors and matrix factorization approaches) and instead focused exclusively on the semantic features of the items. Even though the performance of our methods did not beat the best performing approaches of other teams, our results indicate that it is indeed feasible to create effective recommender systems which fully utilize the content of the items they deal with by utilizing information from the Semantic Web.