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TS-GEAR framework enhances on-demand delivery ad retrieval

By PulseAugur Editorial · [1 sources] ·

Researchers have developed TS-GEAR, a generative retrieval framework designed for on-demand delivery platforms like Taobao Shangou. This system addresses challenges such as geographic reachability, user preference variability across regions, and the need for set-level evaluation rather than individual sequence scoring. TS-GEAR incorporates Geo-Semantic ID for reachability prediction, Query-Geo Target Attention for personalized behavior aggregation, and Group Marginal Policy Optimization for improved candidate set diversity. When deployed on Taobao Shangou, TS-GEAR demonstrated superior performance in ad impressions and revenue compared to existing methods. AI

IMPACT This framework could improve the efficiency and revenue of on-demand delivery services by optimizing ad retrieval.

RANK_REASON The item is a research paper detailing a new framework and its performance evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

TS-GEAR framework enhances on-demand delivery ad retrieval

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The item is a research paper detailing a new framework and its performance evaluation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Hualei Liu ·

    TS-GEAR: Taobao Shangou GEnerative Ad Retrieval

    On-demand delivery platforms such as DoorDash and Uber Eats impose a constraint absent in traditional e-commerce: merchants beyond the user's delivery range are invalid candidates rather than merely low-ranked. Generative retrieval has recently shown strong potential in large-sca…

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