Binarized Collaborative Filtering with Distilling Graph Convolutional Networks

2019-06-05 · Haoyu Wang, Defu Lian, Yong Ge

The efficiency of top-K item recommendation based on implicit feedback are vital to recommender systems in real world, but it is very challenging due to the lack of negative samples and the large number of candidate items. To address the challenges, we firstly introduce an improved Graph Convolutional Network~(GCN) model with high-order feature interaction considered. Then we distill the ranking information derived from GCN into binarized collaborative filtering, which makes use of binary representation to improve the efficiency of online recommendation. However, binary codes are not only hard to be optimized but also likely to incur the loss of information during the training processing. Therefore, we propose a novel framework to convert the binary constrained optimization problem into an equivalent continuous optimization problem with a stochastic penalty. The binarized collaborative filtering model is then easily optimized by many popular solvers like SGD and Adam. The proposed algorithm is finally evaluated on three real-world datasets and shown the superiority to the competing baselines.

📄 PDF Abstract BibTeX arXiv:1906.01829

Code (0)

등록된 구현이 없습니다.

Tasks

Collaborative Filtering Recommendation Systems

Methods 이 논문이 사용한 방법론

Adam 설명 없음

GCN A Graph Convolutional Network, or GCN, is an approach for semi-supervised learning on graph-structured data. It is based on an efficient variant of [convolutional neural…

SGD Stochastic Gradient Descent is an iterative optimization technique that uses minibatches of data to form an expectation of the gradient, rather than the full gradient using…

Disentangled Graph Collaborative Filtering

2020-07-03 · Xiang Wang, Hongye Jin, An Zhang, Xiangnan He 외

Learning informative representations of users and items from the interaction data is of crucial importance to collaborative filtering (CF). Present embedding functions exploit user-item relationships to enrich the repres…

Collaborative Filtering Disentanglement

Compositional Coding for Collaborative Filtering

2019-05-09 · Chenghao Liu, Tao Lu, Xin Wang, Zhiyong Cheng 외

Efficiency is crucial to the online recommender systems. Representing users and items as binary vectors for Collaborative Filtering (CF) can achieve fast user-item affinity computation in the Hamming space, in recent yea…

Collaborative Filtering Recommendation Systems

Neural Causal Graph Collaborative Filtering

2023-07-10 · Xiangmeng Wang, Qian Li, Dianer Yu, Wei Huang 외

Graph collaborative filtering (GCF) has gained considerable attention in recommendation systems by leveraging graph learning techniques to enhance collaborative filtering (CF). One classical approach in GCF is to learn u…

Collaborative Filtering Graph Learning Graph Representation Learning Recommendation Systems+2

Interactive Graph Convolutional Filtering

2023-09-04 · Jin Zhang, Defu Lian, Hong Xie, Yawen Li 외

Interactive Recommender Systems (IRS) have been increasingly used in various domains, including personalized article recommendation, social media, and online advertising. However, IRS faces significant challenges in prov…

Collaborative Filtering Meta-Learning Recommendation Systems Variational Inference

When Product Search Meets Collaborative Filtering: A Hierarchical Heterogeneous Graph Neural Network Approach

2021-08-17 · Xiangkun Yin, Yangyang Guo, Liqiang Nie, Zhiyong Cheng

Personalization lies at the core of boosting the product search system performance. Prior studies mainly resorted to the semantic matching between textual queries and user/product related documents, leaving the user coll…

Collaborative Filtering Graph Neural Network Representation Learning Sentence