Two-stage recommenders first retrieve a small candidate set from a large catalog and then apply a more detailed model to rank only those items. A common offline test instead gives every system the same candidates so that their scoring rules can be compared fairly, but this fixed-candidate protocol may select a differen...
Teresa Zhang· Proceedings of the 20th ACM...· 0 citations
Reasoning over long document contexts remains a challenge for large language models (LLMs). Standard retrieval methods are inherently reactive, bottlenecking agents with complex, cross-document reasoning at inference time, while precomputed structures like knowledge graphs often fall short because they rely on rigid he...
Anikait Singh, Teresa Zhang, Yoonho Lee et al.· 0 citations
Two-stage recommender systems often must choose among same-budget candidate sets before expensive reranking or online evaluation, yet Recall@K, diversity, and coverage do not measure how much fine-state-contingent choice survives retrieval. We define retained personalized headroom as the value of choosing separately fo...
Teresa Zhang· Proceedings of the 20th ACM...· 0 citations
Two-stage recommender systems often must choose among same-budget candidate sets before expensive reranking or online evaluation, yet Recall@K, diversity, and coverage do not measure how much fine-state-contingent choice survives retrieval. We define retained personalized headroom as the value of choosing separately fo...
Teresa Zhang· Proceedings of the 20th ACM...· 0 citations
Two-stage recommenders first retrieve a small candidate set from a large catalog and then apply a more detailed model to rank only those items. A common offline test instead gives every system the same candidates so that their scoring rules can be compared fairly, but this fixed-candidate protocol may select a differen...
Teresa Zhang· Proceedings of the 20th ACM...· 0 citations
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