Retrieval coverage limits LLM rerankers in cold-start recommendation; a learned hybrid fusion improves pool quality but LLM reranking often degrades end-to-end performance while simpler rankers exploit the pool.
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Maxwell Harper and Joseph A
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GREW uses a secret-key-driven green-red item partition and three ranking-integrated modules to embed verifiable watermarks in recommender systems that resist extraction attacks without data injection.
HORIZON creates a cross-domain, long-horizon user modeling benchmark from Amazon Reviews that tests generalization across time, domains, and unseen users, exposing gaps in sequential and LLM-based recommendation models.
For homogeneous agents in multi-agent linear bandits the regret-based TU game is convex with non-empty core containing the Shapley value; for heterogeneous agents a simple regret-based payout lies in the core and satisfies three Shapley axioms.
LLMAR applies LLM reasoning with a self-correction reflection loop to generate semantic user motives for tuning-free recommendations, showing up to 54.6% nDCG@10 gains on a sparse industrial dataset over trained baselines.
Scoring functions are sub-optimal for all utility-fairness trade-offs in ranking under a generic fairness formulation, but semi-greedy post-processing can approach the performance of exhaustive post-processing.
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Graphify automates synthesis of type-safe graph backends via a formal GraphQL-to-Gremlin mapping and O(S) recursive transpilation algorithm supporting CRUD and nested queries.
GloRank reformulates list-wise reranking as token generation over a global item identifier space, using supervised pre-training followed by reinforcement learning to maximize list-wise utility and outperforming baselines on benchmarks and industrial data.
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