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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 11–20 of 113 papers

TitleStatusHype
A Language-Driven Framework for Improving Personalized Recommendations: Merging LLMs with Traditional Algorithms—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
A Deep, Forgetful Novelty-Seeking Movie Recommender Model—0
Construction of Hierarchical Structured Knowledge-based Recommendation Dialogue Dataset and Dialogue System—0
A novel Empirical Bayes with Reversible Jump Markov Chain in User-Movie Recommendation system—0
Collaborative filtering via sparse Markov random fields—0
Submodular Maximization in Clean Linear Time—0
A case study of Empirical Bayes in User-Movie Recommendation system—0
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