On value-driven marketplaces, user-product interactions concentrate heavily around affordable products. Search ranking systems trained on such engagement data therefore learn relevance patterns that primarily reflect the preferences of the dominant value conscious user cohort, limiting their ability to accurately perso...
Shubhendu Pandey, S. Yadav, Virri Pranav et al.· Proceedings of the 35th ACM...· 0 citations
A personalized quality-aware reranking framework that explicitly incorporates product quality into the ranking objective, and proposes a dual-model architecture: Personalized NQD (PNQD), which estimates user-specific dissatisfaction risk for a user–product pair, and Retention-Driven Alpha (RDA), which adaptively contro...
Siddhant Doshi, Krish Rupapara, Bhavuk Singhal et al.· Proceedings of the 35th ACM...· 0 citations
Modern recommendation systems are predominantly optimized for short-term engagement signals such as Click-Through Rate (CTR) and Conversion Rate (CVR), often overlooking post-purchase user satisfaction. As a result, ranking systems can repeatedly promote products that drive immediate interactions but lead to poor user...
Siddhant Doshi, Krish Rupapara, Bhavuk Singhal et al.· Proceedings of the 35th ACM...· 0 citations
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