Collaborative Information Filtering for Effective Cross-Domain Recommendation

Core Concepts

The core message of this paper is that the distortion of user similarity relationships across domains is a key factor causing negative transfer in cross-domain recommendation, and the proposed Collaborative information regularized User Transformation (CUT) framework can effectively alleviate this issue by directly filtering irrelevant source-domain collaborative information.

Abstract

The paper proposes a Collaborative information regularized User Transformation (CUT) framework to address the negative transfer problem in cross-domain recommendation. The key insights are: Negative transfer often occurs due to the distortion of user similarity relationships across domains. Users who have similar preferences in the source domain may have different interests in the target domain, and the irrelevant source-domain collaborative information can degrade the target domain performance. CUT consists of two phases: TARGET phase: Learn the user similarity relationships in the target domain using a single-domain backbone model. TRANSFER phase: Transfer knowledge from the source domain to the target domain, guided by the target-domain user similarity. This includes a user transformation layer to model different user behaviors across domains, and a contrastive loss term to retain the target-domain user similarity relationships. CUT can be seamlessly applied to various single-domain recommendation models as the backbone, extending them to cross-domain tasks without modifying the model structure or loss terms. Extensive experiments on six cross-domain tasks from two real-world datasets show that CUT-enhanced single-domain backbones significantly outperform state-of-the-art cross-domain and single-domain baselines. Further analysis confirms that CUT can effectively alleviate the negative transfer problem.

Stats

The target domain dataset has at least 5 interactions per user and item. The target domain dataset is split 8:1:1 for training, validation, and testing. The source domain dataset is split 8:2 for training and validation.

Quotes

"Irrelevant information from the source domain may instead degrade target domain performance, which is known as the negative transfer problem." "Our proposed CUT framework can be seamlessly applied to various single-domain backbone models without modifying their model structure and loss terms." "Extensive experiments on six cross-domain tasks in two real-world datasets show significant performance improvement of CUT-enhanced single-domain backbones over SOTA cross-domain and single-domain models."

Key Insights Distilled From

Aiming at the Target

by Hanyu Li,Wei... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.20296.pdf

Deeper Inquiries

How can the CUT framework be extended to handle multiple source domains simultaneously

To extend the CUT framework to handle multiple source domains simultaneously, we can introduce a mechanism to incorporate information from each source domain while still maintaining the user similarity relationships in the target domain. One approach could be to modify the user transformation layer to have separate branches for each source domain, allowing the model to learn domain-specific transformations for overlapping users. Additionally, we can adjust the contrastive loss term to consider similarities not only within the target domain but also across multiple source domains. By incorporating information from multiple source domains in a controlled manner, the CUT framework can effectively handle cross-domain recommendation tasks with multiple sources.

What are the potential limitations of the contrastive loss term in CUT, and how can it be further improved

The contrastive loss term in CUT may have limitations in scenarios where the user similarity relationships are not well-defined or when the source and target domains have significant differences. One potential limitation is the sensitivity of the contrastive loss to the choice of hyperparameters, such as the temperature parameter 𝜏. To improve the contrastive loss term, we can explore adaptive methods to adjust the temperature parameter dynamically during training based on the model's performance. Additionally, incorporating additional regularization techniques, such as domain-specific constraints or adaptive weighting of the loss term, can help mitigate the limitations of the contrastive loss and enhance its effectiveness in filtering irrelevant source information.

How can the CUT framework be adapted to cross-domain recommendation tasks with explicit user and item features, beyond just collaborative information

To adapt the CUT framework to cross-domain recommendation tasks with explicit user and item features beyond collaborative information, we can enhance the user transformation layer to incorporate these additional features. By extending the user transformation layer to consider explicit user and item features, the model can learn more comprehensive representations that capture the unique characteristics of users and items in each domain. This extension would involve modifying the input and output dimensions of the user transformation layer to accommodate the explicit features and integrating them into the transformation process. By incorporating explicit user and item features, the CUT framework can enhance its capability to handle diverse data types and improve the accuracy of cross-domain recommendations.