Disentangled Graph Collaborative Filtering (2007.01764v1)

Abstract: Learning informative representations of users and items from the interaction data is of crucial importance to collaborative filtering (CF). Present embedding functions exploit user-item relationships to enrich the representations, evolving from a single user-item instance to the holistic interaction graph. Nevertheless, they largely model the relationships in a uniform manner, while neglecting the diversity of user intents on adopting the items, which could be to pass time, for interest, or shopping for others like families. Such uniform approach to model user interests easily results in suboptimal representations, failing to model diverse relationships and disentangle user intents in representations. In this work, we pay special attention to user-item relationships at the finer granularity of user intents. We hence devise a new model, Disentangled Graph Collaborative Filtering (DGCF), to disentangle these factors and yield disentangled representations. Specifically, by modeling a distribution over intents for each user-item interaction, we iteratively refine the intent-aware interaction graphs and representations. Meanwhile, we encourage independence of different intents. This leads to disentangled representations, effectively distilling information pertinent to each intent. We conduct extensive experiments on three benchmark datasets, and DGCF achieves significant improvements over several state-of-the-art models like NGCF, DisenGCN, and MacridVAE. Further analyses offer insights into the advantages of DGCF on the disentanglement of user intents and interpretability of representations. Our codes are available in https://github.com/xiangwang1223/disentangled_graph_collaborative_filtering.

• The paper introduces DGCF, a model that disentangles user intents to capture detailed user-item relationships.
• It leverages a graph disentangling module with neighbor routing and independence modeling to refine intent-aware representations.
• Empirical tests on Gowalla, Yelp2018, and Amazon-Book show significant improvements in recall and ndcg over state-of-the-art models.

Disentangled Graph Collaborative Filtering: An Overview

The paper "Disentangled Graph Collaborative Filtering" introduces an innovative approach to improve collaborative filtering (CF) by focusing on disentangled representations of users and items at the granularity of user intents. With the increasing significance of personalized recommendations, accurately capturing user preferences becomes crucial. This research devises a new model termed Disentangled Graph Collaborative Filtering (DGCF), which emphasizes refining user-item interaction graphs and generating intent-aware representations.

Key Contributions

The work highlights several significant contributions:

1. Granular User-Item Relationships: Unlike traditional CF methods which model user-item relationships uniformly, DGCF captures the diversity of user intents like passing time, interest, or external influences such as shopping for family. This results in more informative and diversified representations.
2. Graph Disentangling Module: The proposed model utilizes a graph disentangling module that incorporates a neighbor routing mechanism into graph neural networks. This enables the disentanglement of user intents and iteratively refines the interaction graphs, allowing for the extraction of intent-specific information.
3. Independence Modeling: DGCF includes an independence modeling module using distance correlation as a regularizer to ensure the independence of the various intent-aware components. This reduces the semantic redundancy and maximizes the informative content of the representations.
4. Empirical Validation: Extensive experiments on three benchmark datasets—Gowalla, Yelp2018 (revised), and Amazon-Book—demonstrate the significant improvements of DGCF over state-of-the-art models such as NGCF, DisenGCN, and MacridVAE.
5. Interpretability of Representations: By analyzing user intents, the model enhances the interpretability of representations. The disentangled intents can be aligned with user reviews to provide explanatory graphs that offer insights into the reasoning behind user interactions.

Experimental Results

The extensive experimentation highlights that DGCF achieves substantial improvements in both recall and ndcg metrics across all datasets. The advancement is attributed to the model's ability to disentangle user intents effectively, leading to more robust and semantically meaningful representations.

Theoretical and Practical Implications

From a theoretical standpoint, DGCF advances the discourse on representation learning by seamlessly integrating disentangled representation learning and graph neural networks. Practically, this model can be applied to enhance the performance of recommender systems by offering more personalized and explainable recommendations.

Future Developments

The potential future work includes incorporating additional side information, such as user reviews or psychological insights, to further refine user intent modeling. Another promising direction involves exploring the privacy and robustness of representations, aiming to protect sensitive information from being leaked.

In summary, the paper presents a compelling approach that leverages graph disentangling to enhance the efficacy of collaborative filtering. It provides insightful contributions for researchers interested in the nuanced understanding and application of disentangled representations in recommendation systems.