Multi-Objective Recommendation via Multivariate Policy Learning

arXiv:2405.02141v1 Announce Type: cross
Abstract: Real-world recommender systems often need to balance multiple objectives when deciding which recommendations to present to users. These include behavioural signals (e.g. clicks, shares, dwell time), as well as broader objectives (e.g. diversity, fairness). Scalarisation methods are commonly used to handle this balancing task, where a weighted average of per-objective reward signals determines the final score used for ranking. Naturally, how these weights are computed exactly, is key to success for any online platform. We …

abstract arxiv balance cs.ir cs.lg diversity fairness multi-objective multiple multivariate policy recommendation recommendations recommender systems shares systems type via world