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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 71–80 of 113 papers

TitleStatusHype
The Informational Role of Online Recommendations: Evidence from a Field Experiment—0
User Profile Feature-Based Approach to Address the Cold Start Problem in Collaborative Filtering for Personalized Movie Recommendation—0
The Power of Subsampling in Submodular Maximization—0
Thompson Sampling for Combinatorial Semi-bandits with Sleeping Arms and Long-Term Fairness Constraints—0
Transferability Properties of Graph Neural Networks—0
Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text—0
Transforming Movie Recommendations with Advanced Machine Learning: A Study of NMF, SVD,and K-Means Clustering—0
Understanding User Behavior in Carousel Recommendation Systems for Click Modeling and Learning to Rank—0
Unveiling Bias in Fairness Evaluations of Large Language Models: A Critical Literature Review of Music and Movie Recommendation Systems—0
Using Mise-En-Scène Visual Features based on MPEG-7 and Deep Learning for Movie Recommendation—0
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