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COUPA: An Industrial Recommender System for Online to Offline Service Platforms

arXiv 2304.12549 ↗First posted 25 Apr 2023cs.IR cs.LGcited by 0

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Abstract (arXiv)

Aiming at helping users locally discovery retail services (e.g., entertainment and dinning), Online to Offline (O2O) service platforms have become popular in recent years, which greatly challenge current recommender systems. With the real data in Alipay, a feeds-like scenario for O2O services, we find that recurrence based temporal patterns and position biases commonly exist in our scenarios, which seriously threaten the recommendation effectiveness. To this end, we propose COUPA, an industrial system targeting for characterizing user preference with following two considerations: (1) Time aware preference: we employ the continuous time aware point process equipped with an attention mechanism to fully capture temporal patterns for recommendation. (2) Position aware preference: a position selector component equipped with a position personalization module is elaborately designed to mitigate position bias in a personalized manner. Finally, we carefully implement and deploy COUPA on Alipay with a cooperation of edge, streaming and batch computing, as well as a two-stage online serving mode, to support several popular recommendation scenarios. We conduct extensive experiments to demonstrate that COUPA consistently achieves superior performance and has potential to provide intuitive evidences for recommendation

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 53 references that are in the map. First posted on arXiv 25 Apr 2023.

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References · 25

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13 more
  1. 2017Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networkscited by 126
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  12. 2013Using Temporal Data for Making Recommendationscited by 20
  13. 2020A Deep Recurrent Survival Model for Unbiased Rankingcited by 7

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