SPrank: Semantic Path Based Ranking for Top N Recommendations Using Linked Open Data
In most real world scenarios, the ultimate goal of recommender system applications is to suggest a short ranked list of items, namely top N recommendations, that will appeal to the end user. Often, the problem of computing top N recommendations is mainly tackled with a two step approach. The system focuses first on predicting the unknown ratings, which are eventually used to generate a ranked recommendation list. Actually, the top N recommendation task can be directly seen as a ranking problem where the main goal is not to accurately predict ratings but to directly find the best ranked list of items to recommend. In this article we present SPrank, a novel hybrid recommendation algorithm able to compute top N recommendations exploiting freely available knowledge in the Web of Data. In particular, we employ DBpedia, a well known encyclopedic knowledge base in the Linked Open Data cloud, to extract semantic path based features and to eventually compute top N recommendations in a learning to rank fashion. Experiments with three datasets related to different domains (books, music, and movies) prove the effectiveness of our approach compared to state of the art recommendation algorithms.
ACM Trans. Intell. Syst. Technol.