RGCF: Refined Graph Convolution Collaborative Filtering with concise and
expressive embedding
- URL: http://arxiv.org/abs/2007.03383v2
- Date: Sat, 11 Jul 2020 04:32:32 GMT
- Title: RGCF: Refined Graph Convolution Collaborative Filtering with concise and
expressive embedding
- Authors: Kang Liu, Feng Xue, and Richang Hong
- Abstract summary: We develop a new GCN-based Collaborative Filtering model, named Refined Graph convolution Collaborative Filtering(RGCF)
RGCF is more capable for capturing the implicit high-order connectivities inside the graph and the resultant vector representations are more expressive.
We conduct extensive experiments on three public million-size datasets, demonstrating that our RGCF significantly outperforms state-of-the-art models.
- Score: 42.46797662323393
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Graph Convolution Network (GCN) has attracted significant attention and
become the most popular method for learning graph representations. In recent
years, many efforts have been focused on integrating GCN into the recommender
tasks and have made remarkable progress. At its core is to explicitly capture
high-order connectivities between the nodes in user-item bipartite graph.
However, we theoretically and empirically find an inherent drawback existed in
these GCN-based recommendation methods, where GCN is directly applied to
aggregate neighboring nodes will introduce noise and information redundancy.
Consequently, the these models' capability of capturing high-order
connectivities among different nodes is limited, leading to suboptimal
performance of the recommender tasks. The main reason is that the the nonlinear
network layer inside GCN structure is not suitable for extracting non-sematic
features(such as one-hot ID feature) in the collaborative filtering scenarios.
In this work, we develop a new GCN-based Collaborative Filtering model, named
Refined Graph convolution Collaborative Filtering(RGCF), where the construction
of the embeddings of users (items) are delicately redesigned from several
aspects during the aggregation on the graph. Compared to the state-of-the-art
GCN-based recommendation, RGCF is more capable for capturing the implicit
high-order connectivities inside the graph and the resultant vector
representations are more expressive. We conduct extensive experiments on three
public million-size datasets, demonstrating that our RGCF significantly
outperforms state-of-the-art models. We release our code at
https://github.com/hfutmars/RGCF.
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