SPRank: Semantic Path-based Ranking for Top-N Recommendations using Linked Open Data

In most real world scenarios the ultimate goal of recommender system (RS) applications is to suggest a short ranked list of items, namely top-N recommendations, supposed to be the most appealing for the end user. Often, the problem of computing top-N recommendations is mainly tackled with a two steps approach. The system focuses first on predicting the unknown ratings which are eventually used to generated 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 directly find the best ranked list of items to recommend. In this paper, 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. Ex- periments 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.