Efficient Bipartite Graph Embedding Induced by Clustering Constraints

TL;DR

This paper introduces CCBIE, a novel bipartite graph embedding method that uses clustering constraints to improve scalability, accuracy, and efficiency, especially for sparse and large-scale networks.

Contribution

The paper proposes a clustering constraint-based bipartite graph embedding method that enhances scalability, accuracy, and handling of sparse data in large networks.

Findings

Significantly improves prediction accuracy, especially for cold users and items.

Enhances training speed and reduces memory usage on large-scale graphs.

Maintains global graph properties and captures long-range dependencies.

Abstract

Bipartite graph embedding (BGE) maps nodes to compressed embedding vectors that can reflect the hidden topological features of the network, and learning high-quality BGE is crucial for facilitating downstream applications such as recommender systems. However, most existing methods either struggle to efficiently learn embeddings suitable for users and items with fewer interactions, or exhibit poor scalability to handle large-scale networks. In this paper, we propose a Clustering Constraints induced BIpartite graph Embedding (CCBIE) as an integrated solution to both problems. CCBIE facilitates automatic and dynamic soft clustering of items in a top-down manner, and capturing macro-preference information of users through clusters. Specifically, by leveraging the cluster embedding matrix of items, CCBIE calculates the cluster assignment matrix for items and also captures the extent of user preferences across different clusters, thereby elucidating the similarity between users and items on a macro-scale level. CCBIE effectively preserves the global properties of bipartite graphs, maintaining the cluster structure of isomorphic nodes and accounting for long-range dependencies among heterogeneous nodes. Our approach significantly enhances user-item collaborative relation modeling by integrating adaptive clustering for relationship learning, thereby markedly improving prediction performance, particularly benefiting cold users and items. Extensive experiments indicate that CCBIE consistently and significantly improves accuracy over baseline models, particularly on sparse graphs, while also enhancing training speed and reducing memory requirements on large-scale bipartite graphs.