Empirical test on real news histories shows unconstrained LLM reranking amplifies conspiratorial content exposure while prompt constraints can increase ideological diversity with limited relevance cost.
Molar: Multimodal llms with collaborative filtering alignment for enhanced sequential recom- mendation.arXiv preprint arXiv:2412.18176
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UniRec unifies heterogeneous recommendation modalities via specialized encoders, triplet representations, and hierarchical modeling to outperform prior multimodal LLM recommenders by up to 15% on benchmarks.
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LLM-Assisted Reranking to Operationalize Nuanced Objectives in Recommender Systems
Empirical test on real news histories shows unconstrained LLM reranking amplifies conspiratorial content exposure while prompt constraints can increase ideological diversity with limited relevance cost.