OneLoc: Geo-Aware Generative Recommender Systems for Local Life Service
Zhipeng Wei, Kuo Cai, Junda She, Jie Chen, Minghao Chen, Yang Zeng, Qiang Luo, Wencong Zeng, Ruiming Tang, Kun Gai, Guorui Zhou

TL;DR
OneLoc is a geo-aware generative recommender system designed for local life services, effectively integrating geographic information and balancing multiple objectives, leading to significant improvements in GMV and order numbers in Kuaishou App.
Contribution
We introduce OneLoc, a novel geo-aware generative recommendation model that incorporates geographic information and reinforcement learning to optimize local life service recommendations.
Findings
Achieves over 21% GMV increase in deployment.
Improves order numbers by nearly 18%.
Effectively balances user interests and geographic factors.
Abstract
Local life service is a vital scenario in Kuaishou App, where video recommendation is intrinsically linked with store's location information. Thus, recommendation in our scenario is challenging because we should take into account user's interest and real-time location at the same time. In the face of such complex scenarios, end-to-end generative recommendation has emerged as a new paradigm, such as OneRec in the short video scenario, OneSug in the search scenario, and EGA in the advertising scenario. However, in local life service, an end-to-end generative recommendation model has not yet been developed as there are some key challenges to be solved. The first challenge is how to make full use of geographic information. The second challenge is how to balance multiple objectives, including user interests, the distance between user and stores, and some other business objectives. To address…
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Taxonomy
TopicsRecommender Systems and Techniques · Human Mobility and Location-Based Analysis · Context-Aware Activity Recognition Systems
