Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation

TL;DR

This paper introduces CRGCN, a lightweight graph convolutional network that explicitly models the connections between multiple user behaviors in recommendation systems, improving accuracy without increasing complexity.

Contribution

The paper proposes a novel cascading residual graph convolutional network that leverages inter-behavior connections without extra parameters, enhancing multi-behavior recommendation performance.

Findings

CRGCN outperforms state-of-the-art methods on benchmark datasets.

Model effectively captures behavior connections to refine user embeddings.

Leveraging multiple behaviors improves recommendation accuracy.

Abstract

Multi-behavior recommendation exploits multiple types of user-item interactions to alleviate the data sparsity problem faced by the traditional models that often utilize only one type of interaction for recommendation. In real scenarios, users often take a sequence of actions to interact with an item, in order to get more information about the item and thus accurately evaluate whether an item fits personal preference. Those interaction behaviors often obey a certain order, and different behaviors reveal different information or aspects of user preferences towards the target item. Most existing multi-behavior recommendation methods take the strategy to first extract information from different behaviors separately and then fuse them for final prediction. However, they have not exploited the connections between different behaviors to learn user preferences. Besides, they often introduce complex model structures and more parameters to model multiple behaviors, largely increasing the space and time complexity. In this work, we propose a lightweight multi-behavior recommendation model named Cascading Residual Graph Convolutional Network (CRGCN for short), which can explicitly exploit the connections between different behaviors into the embedding learning process without introducing any additional parameters. In particular, we design a cascading residual graph convolutional network structure, which enables our model to learn user preferences by continuously refining user embeddings across different types of behaviors. The multi-task learning method is adopted to jointly optimize our model based on different behaviors. Extensive experimental results on two real-world benchmark datasets show that CRGCN can substantially outperform state-of-the-art methods. Further studies also analyze the effects of leveraging multi-behaviors in different numbers and orders on the final performance.