Improving recommender system navigability through diversification: a case study of IMDb

The Internet Movie Database (IMDb) is the world's largest collection of facts about movies and features large-scale recommendation systems connecting hundreds of thousands of items. In the past, the principal evaluation criterion for such recommender systems has been the rating accuracy prediction for recommendations within the immediate one-hop-neighborhood. Apart from a few isolated studies, the evaluation methodology for recommender systems has so far lacked approaches that quantify and measure the exposure to novel content while navigating a recommender system. As such, little is known about the support for navigation and browsing as methods to explore, browse and discover novel items within these systems. In this article, we study the navigability of IMDb's recommender systems over multiple hops. To this end, we analyze the recommendation networks of IMDb with a two-level approach: First, we study reachability in terms of components, path lengths and a bow-tie analysis. Second, we simulate practical browsing scenarios based on greedy decentralized search. Our results show that the IMDb recommendation networks are not very well-suited for navigation scenarios. To mitigate this, we apply a method for diversifying recommendations by specifically selecting recommendations which improve connectivity but do not compromise relevance. We demonstrate that this leads to improved reachability and navigability in both recommender systems. Our work underlines the importance of navigability and reachability as evaluation dimension of a large movie recommender system and shows up ways to increase navigational diversity.