MultiSlot ReRanker: A Generic Model-based Re-Ranking Framework in Recommendation Systems

• URL: http://arxiv.org/abs/2401.06293v1
• Date: Thu, 11 Jan 2024 23:17:07 GMT
• Title: MultiSlot ReRanker: A Generic Model-based Re-Ranking Framework in Recommendation Systems
• Authors: Qiang Charles Xiao, Ajith Muralidharan, Birjodh Tiwana, Johnson Jia, Fedor Borisyuk, Aman Gupta, Dawn Woodard
• Abstract summary: We propose a generic model-based re-ranking framework, MultiSlot ReRanker, which simultaneously optimize relevance, diversity, and freshness. We have built a multi-slot re-ranking simulator based on OpenAI Gym integrated with the Ray framework.
• Score: 6.0232112783722
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a generic model-based re-ranking framework, MultiSlot ReRanker, which simultaneously optimizes relevance, diversity, and freshness. Specifically, our Sequential Greedy Algorithm (SGA) is efficient enough (linear time complexity) for large-scale production recommendation engines. It achieved a lift of $+6\%$ to $ +10\%$ offline Area Under the receiver operating characteristic Curve (AUC) which is mainly due to explicitly modeling mutual influences among items of a list, and leveraging the second pass ranking scores of multiple objectives. In addition, we have generalized the offline replay theory to multi-slot re-ranking scenarios, with trade-offs among multiple objectives. The offline replay results can be further improved by Pareto Optimality. Moreover, we've built a multi-slot re-ranking simulator based on OpenAI Gym integrated with the Ray framework. It can be easily configured for different assumptions to quickly benchmark both reinforcement learning and supervised learning algorithms.

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