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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 Fairness-aware Hybrid Recommender System—0
A Language-Driven Framework for Improving Personalized Recommendations: Merging LLMs with Traditional Algorithms—0
A Matrix Decomposition Model Based on Feature Factors in Movie Recommendation System—0
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
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