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About this paper
Kepu Zhang, Yuzhe Cai, Yue Shu, Dawei Chen, Jinhai Zhu, Yuan Xu, Zehua Dai, Tongke Ni and 17 more
WeSearch retrieves relevant articles and image-text posts from a changing collection using query-selected clusters, then scores documents individually. A coarse stage narrows candidates, and a feature-aware ranker orders a shortlist. In a one-month full-traffic test, the deployed system increased CTR by 0.68% and reduced retrieval resource cost by 41% and latency by 19 ms versus production. The test does not isolate clustering; Semantic-ID results were limited to a controlled Base-corpus experiment.
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