Repeat-bias-aware Optimization of Beyond-accuracy Metrics for Next Basket Recommendation

TL;DR

This paper introduces a model-agnostic post-processing optimization algorithm to mitigate repeat bias in next basket recommendation systems, enhancing diversity and fairness without significantly sacrificing recall.

Contribution

It proposes a novel repeat-bias-aware optimization method applicable to various NBR models, improving beyond-accuracy metrics like diversity and fairness.

Findings

Effective mitigation of repeat bias in NBR

Improved diversity and fairness metrics

Minimal recall loss achieved

Abstract

In next basket recommendation (NBR) a set of items is recommended to users based on their historical basket sequences. In many domains, the recommended baskets consist of both repeat items and explore items. Some state-of-the-art NBR methods are heavily biased to recommend repeat items so as to maximize utility. The evaluation and optimization of beyond-accuracy objectives for NBR, such as item fairness and diversity, has attracted increasing attention. How can such beyond-accuracy objectives be pursued in the presence of heavy repeat bias? We find that only optimizing diversity or item fairness without considering repeat bias may cause NBR algorithms to recommend more repeat items. To solve this problem, we propose a model-agnostic repeat-bias-aware optimization algorithm to post-process the recommended results obtained from NBR methods with the objective of mitigating repeat bias when optimizing diversity or item fairness. We consider multiple variations of our optimization algorithm to cater to multiple NBR methods. Experiments on three real-world grocery shopping datasets show that the proposed algorithms can effectively improve diversity and item fairness, and mitigate repeat bias at acceptable Recall loss.