Movie Recommendation
24 papers with code • 1 benchmarks • 2 datasets
Evaluates the ability of language models to propose relevant movie recommendations with collaborative filtering data.
Source: BIG-bench
Benchmarks
These leaderboards are used to track progress in Movie Recommendation
| Trend | Dataset | Best Model | Paper | Code | Compare |
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MovieLens
Genre2Movies
Most implemented papers
A Hybrid Variational Autoencoder for Collaborative Filtering
Our approach combines movie embeddings (learned from a sibling VAE network) with user ratings from the Movielens 20M dataset and applies it to the task of movie recommendation.
Collaborative Filtering with Recurrent Neural Networks
We show that collaborative filtering can be viewed as a sequence prediction problem, and that given this interpretation, recurrent neural networks offer very competitive approach.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
Training Compute-Optimal Large Language Models
We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget.
Movie Recommendation System using Sentiment Analysis from Microblogging Data
3ZadeSSG/ContentBased-Movie-Recommendation-using-Sentiment-Analysis
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Traditional approaches in recommendation systems include collaborative filtering and content-based filtering.
MIRA: A Computational Neuro-Based Cognitive Architecture Applied to Movie Recommender Systems
The present project is inspired by the LIDA model to apply it to the process of movie recommendation, the model called MIRA (Movie Intelligent Recommender Agent) presented percentages of precision similar to a traditional model when submitted to the same assay conditions.
No-Regret Learning in Unknown Games with Correlated Payoffs
We consider the problem of learning to play a repeated multi-agent game with an unknown reward function.
Streaming Submodular Maximization under a $k$-Set System Constraint
In this paper, we propose a novel framework that converts streaming algorithms for monotone submodular maximization into streaming algorithms for non-monotone submodular maximization.
The Adapter-Bot: All-In-One Controllable Conversational Model
The dialogue skills can be triggered automatically via a dialogue manager, or manually, thus allowing high-level control of the generated responses.
INSPIRED: Toward Sociable Recommendation Dialog Systems
sweetpeach/Inspired
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EMNLP 2020
To better understand how humans make recommendations in communication, we design an annotation scheme related to recommendation strategies based on social science theories and annotate these dialogs.
