AI RESEARCH
Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments
arXiv CS.LG
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ArXi:2602.23234v3 Announce Type: replace-cross Large-scale commercial search systems optimize for relevance to drive successful sessions that help users find what they are looking for. To maximize relevance, we leverage two complementary objectives: behavioral relevance (results users tend to click or download) and textual relevance (a result's semantic fit to the query). A persistent challenge is the scarcity of expert-provided textual relevance labels relative to abundant behavioral relevance labels.