Title: Adaptive Quality-Diversity Trade-offs for Large-Scale Batch Recommendation
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: recommender system, determinantal point processes, online learning, diversity, serendipity

Abstract: A core research question in recommender systems is to propose batches of highly relevant and diverse items, that is, items personalized to the user's preferences, but which also might get the user out of their comfort zone. This diversity might induce properties of serendipidity and novelty which might increase user engagement or revenue. However, many real-life problems arise in that case: e.g., avoiding to recommend distinct but too similar items to reduce the churn risk, and computational cost for large item libraries, up to millions of items. First, we consider the case when the user feedback model is perfectly observed and known in advance, and introduce an efficient algorithm called B-DivRec combining determinantal point processes and a fuzzy denuding procedure to adjust the degree of item diversity. This helps enforcing a quality-diversity tradeoff throughout the user history. Second, we propose an approach to adaptively tailor the quality-diversity tradeoff to the user, so that diversity in recommendations can be enhanced if it leads to positive feedback, and vice-versa. Finally, we illustrate the performance and versatility of B-DivRec in the two settings on synthetic and real-life data sets on movie recommendation and drug repurposing.

TL;DR: An efficient and flexible algorithm is introduced for large-scale batch recommandation, which adaptively tunes the quality-diversity tradeoff based on user interaction.