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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 1–25 of 113 papers

TitleStatusHype
Training Compute-Optimal Large Language ModelsCode6
On Generative Agents in RecommendationCode2
Scaling Language Models: Methods, Analysis & Insights from Training GopherCode2
LlamaRec: Two-Stage Recommendation using Large Language Models for RankingCode1
Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of ExplanationsCode1
GHRS: Graph-based Hybrid Recommendation System with Application to Movie RecommendationCode1
Simulating User Satisfaction for the Evaluation of Task-oriented Dialogue SystemsCode1
INSPIRED: Toward Sociable Recommendation Dialog SystemsCode1
The Adapter-Bot: All-In-One Controllable Conversational ModelCode1
A Language-Driven Framework for Improving Personalized Recommendations: Merging LLMs with Traditional Algorithms—0
LumiCRS: Asymmetric Contrastive Prototype Learning for Long-Tail Conversational Movie Recommendation—0
Tell Me the Good Stuff: User Preferences in Movie Recommendation Explanations—0
Multi-Selection for Recommendation Systems—0
Movie Recommendation using Web Crawling—0
Large Language Models as Narrative-Driven Recommenders—0
Contextual Bandits with Arm Request Costs and Delays—0
Bayesian Estimation and Tuning-Free Rank Detection for Probability Mass Function Tensors—0
Ducho meets Elliot: Large-scale Benchmarks for Multimodal RecommendationCode0
Attention Overflow: Language Model Input Blur during Long-Context Missing Items Recommendation—0
Movie Recommendation with Poster Attention via Multi-modal Transformer Feature Fusion—0
Transforming Movie Recommendations with Advanced Machine Learning: A Study of NMF, SVD,and K-Means Clustering—0
CherryRec: Enhancing News Recommendation Quality via LLM-driven Framework—0
Harm Mitigation in Recommender Systems under User Preference DynamicsCode0
Large Language Models as Recommender Systems: A Study of Popularity Bias—0
Leveraging Offline Data in Linear Latent Bandits—0
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