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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 101–113 of 113 papers

TitleStatusHype
BloombergGPT: A Large Language Model for Finance—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
Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms: A Case with Bounded Regret—0
Comprehensive Movie Recommendation System—0
Construction of Hierarchical Structured Knowledge-based Recommendation Dialogue Dataset and Dialogue System—0
Contextual Bandits with Arm Request Costs and Delays—0
Could you guess an interesting movie from the posters?: An evaluation of vision-based features on movie poster database—0
DEUX: An Attribute-Guided Framework for Sociable Recommendation Dialog Systems—0
Edge Data Based Trailer Inception Probabilistic Matrix Factorization for Context-Aware Movie Recommendation—0
ExCalibR: Expected Calibration of Recommendations—0
Exponential Family Embeddings—0
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