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Collaborative Filtering

48 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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Latest papers with code

Neural Graph Collaborative Filtering

20 May 2019xiangwang1223/neural_graph_collaborative_filtering

Further analysis verifies the importance of embedding propagation for learning better user and item representations, justifying the rationality and effectiveness of NGCF.

COLLABORATIVE FILTERING

54
20 May 2019

Compositional Coding for Collaborative Filtering

9 May 20193140102441/CCCF

However, CF with binary codes naturally suffers from low accuracy due to limited representation capability in each bit, which impedes it from modeling complex structure of the data.

COLLABORATIVE FILTERING

1
09 May 2019

Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation

29 Apr 2019XinGla/RCF

In this work, we propose Relational Collaborative Filtering (RCF), a general framework to exploit multiple relations between items in recommender system.

COLLABORATIVE FILTERING

2
29 Apr 2019

A Neural Influence Diffusion Model for Social Recommendation

20 Apr 2019PeiJieSun/diffnet

The key idea of our proposed model is that we design a layer-wise influence propagation structure to model how users' latent embeddings evolve as the social diffusion process continues.

COLLABORATIVE FILTERING

13
20 Apr 2019

Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems

25 Mar 2019echo740/DANSER-WWW-19

Social recommendation leverages social information to solve data sparsity and cold-start problems in traditional collaborative filtering methods.

COLLABORATIVE FILTERING

3
25 Mar 2019

Knowledge Graph Convolutional Networks for Recommender Systems

18 Mar 2019hwwang55/KGCN

To alleviate sparsity and cold start problem of collaborative filtering based recommender systems, researchers and engineers usually collect attributes of users and items, and design delicate algorithms to exploit these additional information.

COLLABORATIVE FILTERING

49
18 Mar 2019

Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation

23 Jan 2019hwwang55/KGCN

Collaborative filtering often suffers from sparsity and cold start problems in real recommendation scenarios, therefore, researchers and engineers usually use side information to address the issues and improve the performance of recommender systems.

COLLABORATIVE FILTERING KNOWLEDGE GRAPH EMBEDDING KNOWLEDGE GRAPHS MULTI-TASK LEARNING

49
23 Jan 2019

Unifying Topic, Sentiment & Preference in an HDP-Based Rating Regression Model for Online Reviews

19 Dec 2018tonyrivermsfly/TSPRA

TSPRA combines topics (i. e. product aspects), word sentiment and user preference as regression factors, and is able to perform topic clustering, review rating prediction, sentiment analysis and what we invent as "critical aspect" analysis altogether in one framework.

COLLABORATIVE FILTERING ONLINE REVIEW RATING SENTIMENT ANALYSIS

0
19 Dec 2018

Sequential Variational Autoencoders for Collaborative Filtering

25 Nov 2018noveens/svae_cf

We introduce a recurrent version of the VAE, where instead of passing a subset of the whole history regardless of temporal dependencies, we rather pass the consumption sequence subset through a recurrent neural network.

COLLABORATIVE FILTERING

19
25 Nov 2018

Towards Large Scale Training Of Autoencoders For Collaborative Filtering

30 Aug 2018amoussawi/recoder

In this paper, we apply a mini-batch based negative sampling method to efficiently train a latent factor autoencoder model on large scale and sparse data for implicit feedback collaborative filtering.

COLLABORATIVE FILTERING

24
30 Aug 2018