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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 31–40 of 113 papers

TitleStatusHype
Language Models as Recommender Systems: Evaluations and Limitations—0
CherryRec: Enhancing News Recommendation Quality via LLM-driven Framework—0
Graph and graphon neural network stability—0
Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender Systems—0
Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies—0
Fast Distributed Submodular Cover: Public-Private Data Summarization—0
Greed is Good: Near-Optimal Submodular Maximization via Greedy Optimization—0
Group Movie Selection using Multi-channel Emotion Recognition—0
Collaborative filtering via sparse Markov random fields—0
A Fairness-aware Hybrid Recommender System—0
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