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Collaborative Filtering

79 papers with code · Miscellaneous

Collaborative filtering is a recommendation system that uses user's past behaviour (items previously purchased or selected and/or numerical ratings given to those items) as well as similar decisions made by other users. This model is then used to predict items (or ratings for items) that the user may have an interest in.

( Image credit: Neural Collaborative Filtering )

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Greatest papers with code

StarSpace: Embed All The Things!

12 Sep 2017facebookresearch/ParlAI

A framework for training and evaluating AI models on a variety of openly available dialogue datasets.

COLLABORATIVE FILTERING TEXT CLASSIFICATION WORD EMBEDDINGS

Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems

25 Sep 2019facebookresearch/dlrm

In this work, we propose mixed dimension embedding layers in which the dimension of a particular embedding vector can depend on the frequency of the item.

CLICK-THROUGH RATE PREDICTION COLLABORATIVE FILTERING

Training Deep AutoEncoders for Collaborative Filtering

5 Aug 2017NVIDIA/DeepRecommender

Our model is based on deep autoencoder with 6 layers and is trained end-to-end without any layer-wise pre-training.

COLLABORATIVE FILTERING

On the Difficulty of Evaluating Baselines: A Study on Recommender Systems

4 May 2019srendle/libfm

Numerical evaluations with comparisons to baselines play a central role when judging research in recommender systems.

COLLABORATIVE FILTERING

Neural Collaborative Filtering

ACM 2017 hexiangnan/neural_collaborative_filtering

When it comes to model the key factor in collaborative filtering -- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items.

COLLABORATIVE FILTERING SPEECH RECOGNITION

Graph Convolutional Matrix Completion

7 Jun 2017tkipf/gae

We consider matrix completion for recommender systems from the point of view of link prediction on graphs.

COLLABORATIVE FILTERING LINK PREDICTION MATRIX COMPLETION

Recurrent Neural Networks with Top-k Gains for Session-based Recommendations

ICLR 2018 hidasib/GRU4Rec

RNNs have been shown to be excellent models for sequential data and in particular for data that is generated by users in an session-based manner.

COLLABORATIVE FILTERING DATA AUGMENTATION SESSION-BASED RECOMMENDATIONS

RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems

9 Mar 2018hwwang55/RippleNet

To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance.

CLICK-THROUGH RATE PREDICTION COLLABORATIVE FILTERING

Variational Autoencoders for Collaborative Filtering

16 Feb 2018dawenl/vae_cf

This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research. We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation.

BAYESIAN INFERENCE COLLABORATIVE FILTERING LANGUAGE MODELLING

Collaborative Filtering with Recurrent Neural Networks

26 Aug 2016rdevooght/sequence-based-recommendations

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.

COLLABORATIVE FILTERING