Neural Graph Matching based Collaborative Filtering
- URL: http://arxiv.org/abs/2105.04067v1
- Date: Mon, 10 May 2021 01:51:46 GMT
- Title: Neural Graph Matching based Collaborative Filtering
- Authors: Yixin Su and Rui Zhang and Sarah Erfani and Junhao Gan
- Abstract summary: We identify two different types of attribute interactions, inner and cross interactions.
Existing models do not distinguish these two types of attribute interactions.
We propose a neural Graph Matching based Collaborative Filtering model (GMCF)
Our model outperforms state-of-the-art models.
- Score: 13.086302251856756
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: User and item attributes are essential side-information; their interactions
(i.e., their co-occurrence in the sample data) can significantly enhance
prediction accuracy in various recommender systems. We identify two different
types of attribute interactions, inner interactions and cross interactions:
inner interactions are those between only user attributes or those between only
item attributes; cross interactions are those between user attributes and item
attributes. Existing models do not distinguish these two types of attribute
interactions, which may not be the most effective way to exploit the
information carried by the interactions. To address this drawback, we propose a
neural Graph Matching based Collaborative Filtering model (GMCF), which
effectively captures the two types of attribute interactions through modeling
and aggregating attribute interactions in a graph matching structure for
recommendation. In our model, the two essential recommendation procedures,
characteristic learning and preference matching, are explicitly conducted
through graph learning (based on inner interactions) and node matching (based
on cross interactions), respectively. Experimental results show that our model
outperforms state-of-the-art models. Further studies verify the effectiveness
of GMCF in improving the accuracy of recommendation.
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