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Movie Recommendation

Evaluates the ability of language models to propose relevant movie recommendations with collaborative filtering data.

Source: BIG-bench

Papers

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Showing 91–100 of 113 papers

TitleStatusHype
A Multi-source Graph Representation of the Movie Domain for Recommendation Dialogues Analysis—0
Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies—0
Submodular Maximization in Clean Linear Time—0
A novel Empirical Bayes with Reversible Jump Markov Chain in User-Movie Recommendation system—0
A Survey of Point-of-interest Recommendation in Location-based Social Networks—0
Attention Overflow: Language Model Input Blur during Long-Context Missing Items Recommendation—0
Bayesian Estimation and Tuning-Free Rank Detection for Probability Mass Function Tensors—0
Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender Systems—0
CherryRec: Enhancing News Recommendation Quality via LLM-driven Framework—0
Collaborative filtering via sparse Markov random fields—0
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