Recommender systems aim to provide users with accurate item suggestions in a personalized fashion, but struggle in the case of cold start users, for whom there is a scarcity of preference data. User preferences can be either explicitly stated by the users — often by means of ratings —, or implicitly acquired by a system — for instance by mining text reviews, search queries, and purchase records. Recommendation methods have been mostly designed to deal with numerical ratings. However, real scenarios with user preferences expressed in the form of binary and unary (positive-only) feedback, e.g. the thumbs up/down in YouTube, and the likes in Facebook, are increasingly popular, and make the user cold start problem even more challenging. To address the cold start with positive-only feedback situations, we propose to exploit data additional to user preferences by means of specialized hybrid recommendation methods. In particular, we investigate a number of graph-based and matrix factorization recommendation models that jointly exploit user preferences and item semantic metadata automatically extracted from the well-known knowledge graph of DBpedia. Following a rigorous evaluation methodology for cold start, we empirically compare the above hybrid recommendation models on a Facebook dataset containing users likes for items in three different domains, namely books, movies and music. The achieved experimental results show that the semantics-aware hybrid approaches we propose outperform content-based and collaborative filtering baselines. In addition to recommendation accuracy, in our evaluation we also consider individual and aggregate diversity of recommendations as key quality factors in the users’ satisfaction.
Dealing with Pure New User Cold-Start Problem in Recommendation System Based on Linked Open Data and Social Network Features
