A New System-Wide Diversity Measure for Recommendations with Efficient Algorithms

Abstract

Recommender systems often operate on item catalogs clustered by genres and user bases that have natural clusterings into user types by demographic or psychographic attributes. Prior work on system-wide diversity has mainly focused on defining intent-aware metrics among such categories and maximizing relevance of the resulting recommendations, but this work has not combined the notions of diversity from the two points of view of items and users. In this work, we do the following: (1) We introduce two new system-wide diversity metrics to simultaneously address the problems of diversifying the categories of items that each user sees, diversifying the types of users that each item is shown, and maintaining high recommendation quality. We model this as a subgraph selection problem on the bipartite graph of candidate recommendations between users and items. (2) In the case of disjoint item categories and user types, we show that the resulting problems can be solved exactly in polynomial time, by a reduction to a minimum cost flow problem. (3) In the case of nondisjoint categories and user types, we prove NP-completeness of the objective and present efficient approximation algorithms using the submodularity of the objective. (4) Finally, we validate the effectiveness of our algorithms on the MovieLens-1m dataset and show that algorithms designed for our objective also perform well on sales diversity metrics and even on some intent-aware diversity metrics. Our experimental results justify the validity of our new composite diversity metrics.