{"total":2,"items":[{"citing_arxiv_id":"2605.27429","ref_index":19,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Ocean4Rec: Offline LLM-Derived OCEAN Profiles for Request-Time VOD Reranking","primary_cat":"cs.IR","submitted_at":"2026-05-22T04:04:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Ocean4Rec uses offline LLM to create OCEAN profiles for items and time-decayed user profiles for request-time numeric reranking, improving NDCG@20 by 7.6% and 61.5% over base+recency in offline VOD evaluations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16615","ref_index":6,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Learning What Evaluators Value: A Reliable Approach to Modeling Evaluator Preferences","primary_cat":"cs.LG","submitted_at":"2026-05-15T20:33:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Presents a robust algorithm for learning any coordinate-wise non-decreasing evaluator preference function, with theoretical guarantees that it matches linear performance when linearity holds.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}