Retraining A Graph-based Recommender with Interests Disentanglement

TL;DR

This paper introduces a novel retraining framework called Disentangled Incremental Learning (DIL) for graph-based recommender systems, which effectively captures both long-term and recent user preferences by disentangling historical and new interaction signals.

Contribution

The paper proposes a generic DIL framework that disentangles historical and new preferences in graph-based recommenders, improving their ability to adapt to evolving user behaviors.

Findings

DIL improves recommendation accuracy on benchmark datasets.

DIL effectively captures user preference dynamics.

DIL enhances robustness when combined with base models like LightGCN and NGCF.

Abstract

In a practical recommender system, new interactions are continuously observed. Some interactions are expected, because they largely follow users' long-term preferences. Some other interactions are indications of recent trends in user preference changes or marketing positions of new items. Accordingly, the recommender needs to be periodically retrained or updated to capture the new trends, and yet not to forget the long-term preferences. In this paper, we propose a novel and generic retraining framework called Disentangled Incremental Learning (DIL) for graph-based recommenders. We assume that long-term preferences are well captured in the existing model, in the form of model parameters learned from past interactions. New preferences can be learned from the user-item bipartite graph constructed using the newly observed interactions. In DIL, we design an Information Extraction Module to extract historical preferences from the existing model. Then we blend the historical and new preferences in the form of node embeddings in the new graph, through a Disentanglement Module. The essence of the disentanglement module is to decorrelate the historical and new preferences so that both can be well captured, via carefully designed losses. Through experiments on three benchmark datasets, we show the effectiveness of DIL in capturing dynamics of useritem interactions. We also demonstrate the robustness of DIL by attaching it to two base models - LightGCN and NGCF.