SalesSim benchmarks MLLMs as retail user simulators, finds gaps in persona adherence and over-persuasion, and introduces UserGRPO RL to raise decision alignment by 13.8%.
InProceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’15)
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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.
Entity representations learned from text via link prediction generalize to unseen entities and transfer to classification and retrieval with reported gains of 22% MRR, 16% accuracy, and 8.8% NDCG@10.
Behavior-guided calibration converts co-user overlap into signed evidence applied only to multimodal recommender shortlists and yields consistent gains on Amazon Baby, Sports, and Electronics datasets.
H-MAPS uses a three-layered hierarchical memory to infer a reader's background and intent from implicit behaviors, generating profile-specific questions and on-device literature retrieval, as shown when NLP and HCI researchers receive different recommendations for the same paper.
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SalesSim: Benchmarking and Aligning Multimodal Language Models as Retail User Simulators
SalesSim benchmarks MLLMs as retail user simulators, finds gaps in persona adherence and over-persuasion, and introduces UserGRPO RL to raise decision alignment by 13.8%.
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Inductive Entity Representations from Text via Link Prediction
Entity representations learned from text via link prediction generalize to unseen entities and transfer to classification and retrieval with reported gains of 22% MRR, 16% accuracy, and 8.8% NDCG@10.