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Collaborative Filtering

39 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.

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Greatest papers with code

StarSpace: Embed All The Things!

12 Sep 2017facebookresearch/ParlAI

We present StarSpace, a general-purpose neural embedding model that can solve a wide variety of problems: labeling tasks such as text classification, ranking tasks such as information retrieval/web search, collaborative filtering-based or content-based recommendation, embedding of multi-relational graphs, and learning word, sentence or document level embeddings. In each case the model works by embedding those entities comprised of discrete features and comparing them against each other -- learning similarities dependent on the task.

COLLABORATIVE FILTERING TEXT CLASSIFICATION WORD EMBEDDINGS

Training Deep AutoEncoders for Collaborative Filtering

5 Aug 2017NVIDIA/DeepRecommender

This paper proposes a novel model for the rating prediction task in recommender systems which significantly outperforms previous state-of-the art models on a time-split Netflix data set. Our model is based on deep autoencoder with 6 layers and is trained end-to-end without any layer-wise pre-training.

COLLABORATIVE FILTERING

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. Interaction data such as movie ratings can be represented by a bipartite user-item graph with labeled edges denoting observed ratings.

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. The use of RNNs provides impressive performance benefits over classical methods in session-based recommendations.

COLLABORATIVE FILTERING SESSION-BASED RECOMMENDATIONS

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. In particular we study how the long short-term memory (LSTM) can be applied to collaborative filtering, and how it compares to standard nearest neighbors and matrix factorization methods on movie recommendation.

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. We also provide extended experiments comparing the multinomial likelihood with other commonly used likelihood functions in the latent factor collaborative filtering literature and show favorable results.

BAYESIAN INFERENCE COLLABORATIVE FILTERING LANGUAGE MODELLING

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. This paper considers the knowledge graph as the source of side information.

CLICK-THROUGH RATE PREDICTION COLLABORATIVE FILTERING

Matrix Factorization on GPUs with Memory Optimization and Approximate Computing

11 Aug 2018cuMF/cumf_als

Matrix factorization (MF) discovers latent features from observations, which has shown great promises in the fields of collaborative filtering, data compression, feature extraction, word embedding, etc. Current MF implementations are either optimized for a single machine or with a need of a large computer cluster but still are insufficient.

COLLABORATIVE FILTERING

Hybrid Recommender System based on Autoencoders

24 Jun 2016fstrub95/Autoencoders_cf

A standard model for Recommender Systems is the Matrix Completion setting: given partially known matrix of ratings given by users (rows) to items (columns), infer the unknown ratings. In the last decades, few attempts where done to handle that objective with Neural Networks, but recently an architecture based on Autoencoders proved to be a promising approach.

COLLABORATIVE FILTERING MATRIX COMPLETION

Hybrid Collaborative Filtering with Autoencoders

2 Mar 2016fstrub95/Autoencoders_cf

Such algorithms look for latent variables in a large sparse matrix of ratings. In this paper, we introduce a Collaborative Filtering Neural network architecture aka CFN which computes a non-linear Matrix Factorization from sparse rating inputs and side information.

COLLABORATIVE FILTERING