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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
Tell Me the Good Stuff: User Preferences in Movie Recommendation Explanations—0
Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization—0
Tensors, Learning, and 'Kolmogorov Extension' for Finite-alphabet Random Vectors—0
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
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