Improving recommendation diversity and serendipity with an ontology-based algorithm for cold start environments

Every real-life environments where users interact with items (products, films, research expert profiles) have several development phases. In the Cold-start phase, there are almost no interactions among users and items content-based recommendation systems (RS) can only recommend based on matching the attributes of the items. In the transition state, items start to collect user interactions but still a significant number of items have too small number of interactions, RS does not allow users to discover cold items. In a regular state, where most of the items in the system have enough interactions, the recommendations often suffer from low diversity of the items within a single recommendation. This article proposes a general recommendation algorithm based on Ontological-similarity, which is designed to address all the above problems. Our experiments show that recommendations generated by our approach are consistently better in all environment development phases and increase the success rate of recommendations by almost 50% measured using ontology-aware recall, which is also introduced in this article.