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Mixture of Semantic Attention Regimes (MoSAR) is introduced, which learns such an adaptive, controlled-decay geometry over query--key interactions, suggesting that it is not only adaptive, but also amenable to low-cost approximation at inference time.
Agentic Conversational Recommender Systems (ACRSs) are designed to recommend through multi-turn dialogue with users whose needs are not fully formed at the outset. However, their evaluation almost exclusively relies on user simulators that instantiate users with clear, pre-formed needs, reducing the interaction to a re...
This work introduces a family of open-weight user simulation models capable of generalizing across diverse e-commerce domains and operationalizes three distinct behavioral stereotypes, highlighting the necessity of a scalable framework for rigorously stress-testing the next generation of conversational agents against r...
The proposed REKALM, a comprehensive integration framework for enhancing LLM-based recommenders through knowledge integration, demonstrates that augmenting LLMs with lexicalized, domain-specific knowledge is an effective system-level strategy for advancing the next generation of recommender systems.
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