Causal Deconfounding via Confounder Disentanglement for Enhancing Dual-Target Cross-Domain Recommendation Accuracy

מושגי ליבה

The proposed Causal Deconfounding framework via Confounder Disentanglement (CD2CDR) effectively decouples observed single-domain and cross-domain confounders, eliminates their negative effects on capturing comprehensive user preferences, and incorporates their positive effects to enhance recommendation accuracy in both data-richer and data-sparser domains.

תקציר

The paper proposes a novel Causal Deconfounding framework via Confounder Disentanglement (CD2CDR) for dual-target Cross-Domain Recommendation (CDR). The key highlights are:

CD2CDR first pre-trains a backbone model to obtain disentangled domain-independent, domain-specific, and domain-shared user preferences.

It then proposes a confounder disentanglement module to effectively decouple observed single-domain confounders (SDCs) and cross-domain confounders (CDCs). The SDCs are disentangled using a dual adversarial structure, while the CDCs are decoupled via half-sibling regression.

CD2CDR further introduces a causal deconfounding module to eliminate the negative effects of the disentangled observed confounders on capturing comprehensive user preferences, while preserving their positive effects to enhance recommendation accuracy in both domains.

Extensive experiments on five real-world datasets demonstrate that CD2CDR significantly outperforms state-of-the-art baselines, with an average increase of 6.64% and 8.92% in HR@10 and NDCG@10, respectively.

סטטיסטיקה

"In recent years, dual-target Cross-Domain Recommendation (CDR) has been proposed to capture comprehensive user preferences in order to ultimately enhance the recommendation accuracy in both data-richer and data-sparser domains simultaneously."
"The observed confounders can be categorized into two types, i.e., single-domain confounder (SDC) and cross-domain confounder (CDC)."
"Extensive experiments conducted on five real-world datasets show that our CD2CDR outperforms the best-performing state-of-the-art baseline with an average increase of 6.64% and 8.92% w.r.t. HR@10 and NDCG@10, respectively."

ציטוטים

"Cross-domain confounders have both positive and negative impacts on predicting user-item interactions in both domains."
"As long as observed confounders are accurately disentangled, they can facilitate the design of effective deconfounding module for more precise deconfounding."
"The essence of our causal deconfounding module lies in blocking the backdoor paths C →Z, allowing the model to concentrate on the direct effects of users' true preferences on the predicted interactions Z →Y, and disregard the interference of confounders on these preferences."

תובנות מפתח מזוקקות מ:

Causal Deconfounding via Confounder Disentanglement for Dual-Target Cross-Domain Recommendation

by Jiajie Zhu,Y... ב- arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11180.pdf

שאלות מעמיקות

How can the proposed CD2CDR framework be extended to handle more than two target domains in a multi-target CDR scenario

In order to extend the proposed CD2CDR framework to handle more than two target domains in a multi-target CDR scenario, several modifications and enhancements can be implemented:

Expansion of Confounder Disentanglement: The framework can be adapted to disentangle observed confounders across multiple domains simultaneously. This would involve developing a more sophisticated mechanism to identify and decouple confounders that affect user preferences and interactions in each target domain.

Scalability of Causal Deconfounding Module: The causal deconfounding module can be scaled to accommodate the increased complexity of multiple target domains. This may involve optimizing the backdoor adjustment process to handle a larger number of observed confounders and their effects on comprehensive user preferences.

Integration of Multi-Domain Interaction Prediction: The interaction prediction network can be enhanced to predict user-item interactions across multiple target domains. This would require incorporating the disentangled user preferences and observed confounders from each domain into the prediction process.

Cluster Centroids for Multiple Domains: The clustering of observed confounders can be extended to include cluster centroids for each target domain in the multi-target CDR scenario. This would enable the framework to capture domain-specific confounders and their effects on user preferences more effectively.

By implementing these adaptations, the CD2CDR framework can be effectively extended to handle more than two target domains in a multi-target CDR scenario, providing comprehensive recommendations across a diverse range of domains.

What are the potential limitations of the current confounder disentanglement approach, and how can it be further improved to handle more complex real-world scenarios

The current confounder disentanglement approach in the CD2CDR framework may have some limitations that could be addressed for further improvement:

Complexity of Confounders: Real-world scenarios may involve highly complex confounders that are challenging to disentangle using the current approach. To overcome this limitation, advanced machine learning techniques such as deep learning models or reinforcement learning algorithms could be explored.

Interactions between Confounders: The current approach may not fully capture the interactions between different observed confounders and their combined effects on user preferences. Enhancements could involve developing a more comprehensive model that considers the interplay of multiple confounders in the recommendation process.

Generalization to Diverse Datasets: The current approach may be tailored to specific datasets and may not generalize well to diverse datasets with varying characteristics. To improve generalization, the framework could be adapted to handle different types of data sources and user behaviors.

Robustness to Noisy Data: The confounder disentanglement process may be sensitive to noisy or incomplete data, leading to suboptimal results. Techniques for data cleaning, feature engineering, and outlier detection could be integrated to enhance the robustness of the approach.

By addressing these potential limitations, the confounder disentanglement approach in the CD2CDR framework can be further improved to handle more complex real-world scenarios effectively.

Given the importance of preserving the positive effects of observed confounders, how can the proposed causal deconfounding module be adapted to other recommendation tasks beyond dual-target CDR

To adapt the proposed causal deconfounding module to other recommendation tasks beyond dual-target CDR, the following strategies can be considered:

Task-Specific Feature Engineering: Customize the feature engineering process to suit the specific characteristics of the recommendation task. This may involve identifying relevant confounders and their impacts on user preferences in the context of the new task.

Domain Adaptation Techniques: Implement domain adaptation techniques to transfer knowledge from the dual-target CDR setting to the new recommendation task. This can help preserve the positive effects of observed confounders while eliminating their negative impacts in the new domain.

Transfer Learning: Utilize transfer learning methods to leverage the insights gained from the causal deconfounding module in dual-target CDR and apply them to the new recommendation task. This can facilitate the adaptation of the module to different datasets and domains.

Evaluation and Validation: Conduct thorough evaluation and validation experiments to assess the performance of the adapted causal deconfounding module in the new recommendation task. This includes comparing the recommendation accuracy with baseline models and analyzing the impact of observed confounders on user-item interactions.

By incorporating these strategies, the proposed causal deconfounding module can be effectively adapted to various recommendation tasks beyond dual-target CDR, ensuring the preservation of positive effects of observed confounders and enhancing the overall recommendation accuracy.