Adaptive Quality-Diversity Trade-offs for Large-Scale Batch Recommendation

TL;DR

This paper introduces B-DivRec, an efficient algorithm for personalized batch recommendations that balances relevance and diversity, adapting to user feedback to enhance engagement and reduce churn.

Contribution

The paper presents a novel algorithm combining determinantal point processes and fuzzy denuding for quality-diversity trade-offs, with adaptive tuning based on user feedback.

Findings

B-DivRec effectively balances relevance and diversity in recommendations.

Adaptive adjustment improves user engagement and reduces churn.

Validated on synthetic and real-world datasets for movies and drug repurposing.

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 trade-off throughout the user history. Second, we propose an approach to adaptively tailor the quality-diversity trade-off 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.