Adaptive Coordinators and Prompts on Heterogeneous Graphs for Cross-Domain Recommendations

المفاهيم الأساسية

This paper proposes HAGO, a novel data-centric framework that leverages heterogeneous adaptive graph coordinators and graph prompting to address the challenges of cross-domain recommendation by aligning representations and transferring knowledge across multiple domains.

الملخص

• Bibliographic Information: Hengyu Zhang, Chunxu Shen, Xiangguo Sun, Jie Tan, Yu Rong, Chengzhi Piao, Hong Cheng, and Lingling Yi. 2018. Adaptive Coordinators and Prompts on Heterogeneous Graphs for Cross-Domain Recommendations. In Proceedings of Make sure to enter the correct conference title from your rights confirmation emai (Conference acronym ’XX). ACM, New York, NY, USA, 12 pages. https://doi.org/XXXXXXX.XXXXXXX
• Research Objective: This paper aims to improve cross-domain recommendation by addressing the limitations of model-centric approaches and proposing a data-centric framework called HAGO.
• Methodology: HAGO utilizes heterogeneous adaptive graph coordinators to integrate multi-domain graphs into a unified structure for collaborative pre-training. It then employs graph prompting to transfer the learned multi-domain knowledge to the target domain for enhanced recommendation accuracy. The framework is evaluated on two real-world datasets using various graph-based models and pre-training techniques.
• Key Findings: Experimental results demonstrate that HAGO consistently outperforms state-of-the-art methods in multi-domain recommendation scenarios. The study highlights the effectiveness of graph coordinators in mitigating negative transfer and the compatibility of HAGO with various backbone networks and pre-training algorithms.
• Main Conclusions: HAGO offers a promising solution for cross-domain recommendation by effectively aligning representations and transferring knowledge across multiple domains. The data-centric approach of HAGO overcomes the limitations of model-centric methods and achieves superior performance.
• Significance: This research contributes to the field of recommender systems by introducing a novel data-centric framework for cross-domain recommendation. The proposed HAGO framework has the potential to enhance recommendation accuracy in real-world applications where users interact with items across multiple domains.
• Limitations and Future Research: The paper acknowledges that the optimal number of graph coordinators may vary depending on the specific dataset and task. Future research could explore methods for automatically determining the optimal number of coordinators. Additionally, investigating the application of HAGO to other related tasks, such as cross-domain search and retrieval, could be a promising direction.

الإحصائيات

HAGO achieves 1.20% and 1.28% improvements on Recall@10.
HAGO achieves 1.44% and 1.67% improvements on HR@10.
HAGO achieves 1.32% and 1.64% improvements on NDCG@10.
HAGO achieves 0.59% and 2.57% improvements on MRR.

اقتباسات

"Although these methods have achieved notable success, some later theoretical studies [2] have found that the performance ceiling of model-centric methods is strictly limited by the intrinsic discrepancy among various domains, indicating that we might not further improve these methods unless we can find some data-level approaches to narrow down the natural gap among various graph domains."
"Inspired by the recent success of graph prompting in its powerful data operation capability [33, 35, 52], we go beyond the previous model-centric paradigm and hope to bridge gaps between diverse graph datasets in a data-centric perspective."

الرؤى الأساسية المستخلصة من

Adaptive Coordinators and Prompts on Heterogeneous Graphs for Cross-Domain Recommendations

by Hengyu Zhang... في arxiv.org 10-16-2024

https://arxiv.org/pdf/2410.11719.pdf

استفسارات أعمق

How can the HAGO framework be adapted to handle dynamic user preferences and evolving item characteristics in real-time recommendation scenarios?

The HAGO framework, while demonstrating strong performance in cross-domain recommendations, primarily operates under a static setting assuming fixed user preferences and item characteristics during the pre-training phase. To adapt to the dynamic nature of real-time recommendations, several modifications can be considered:

Incremental Learning on Coordinators: Instead of fixed pre-trained embeddings, allow the graph coordinators to continuously learn and evolve with new interaction data. This can be achieved through incremental learning techniques applied on the coordinator embeddings, capturing the shifts in user preferences and item trends over time.
Dynamic Edge Weight Adjustment: Implement mechanisms for dynamically adjusting the edge weights between coordinators and nodes in real-time. This could involve incorporating temporal information into the edge weight calculation (Equation 3), allowing the model to emphasize recent interactions and adapt to evolving user interests.
Short-Term Preference Integration: Incorporate short-term user behavior signals, such as recent clicks or browsing history, into the recommendation process. This can be achieved by introducing additional graph components or features that capture these short-term interactions, complementing the long-term preferences learned during pre-training.
Periodic Refinement of Pre-trained Embeddings: Regularly refine the pre-trained embeddings through periodic re-training on updated datasets. This ensures that the model stays abreast of the evolving data distribution and maintains its effectiveness in capturing dynamic user preferences and item characteristics.
By incorporating these adaptations, HAGO can transition from a static model to a dynamic system capable of providing more timely and relevant recommendations in real-time scenarios.

Could the reliance on pre-training potentially limit the adaptability of HAGO to new or rapidly changing domains with limited data?

Yes, the reliance on pre-training could pose challenges for HAGO's adaptability to new or rapidly changing domains with limited data. Here's why:

Data Scarcity Impedes Pre-training: Pre-training typically demands substantial data to learn meaningful representations. In new or rapidly evolving domains with limited data, the pre-training process might not effectively capture the underlying data distribution, leading to suboptimal initialization for the recommendation task.
Cold-Start Problem: Pre-trained embeddings are inherently reliant on past interactions. For new users or items in a domain, the lack of historical data makes it difficult to leverage the pre-trained knowledge, exacerbating the cold-start problem.
Domain Shift: Pre-training on source domains might not generalize well to a target domain with significant differences in user behavior or item characteristics. This domain shift can hinder the transferability of pre-trained knowledge and impact recommendation accuracy.
To mitigate these limitations, consider the following strategies:

Few-Shot Learning Techniques: Explore few-shot learning approaches that can adapt the pre-trained model to new domains with limited data. This involves fine-tuning the model on a small set of labeled examples from the target domain.
Meta-Learning: Leverage meta-learning to train the model on a distribution of tasks, enabling it to quickly adapt to new domains with minimal data.
Hybrid Approaches: Combine pre-training with other techniques, such as content-based filtering or collaborative filtering, to compensate for data scarcity in new domains.
By incorporating these strategies, HAGO can enhance its adaptability and effectiveness in handling new or rapidly changing domains with limited data.

What are the ethical implications of using cross-domain data for recommendation, particularly concerning user privacy and potential biases in the recommendations?

Using cross-domain data for recommendation, while offering potential benefits, raises significant ethical concerns regarding user privacy and potential biases:
Privacy Concerns:

Data Leakage: Combining data from multiple domains increases the risk of inadvertently revealing sensitive user information. For example, a user's purchase history in one domain, when combined with their browsing data in another, might reveal private information they did not intend to share.
Inference Attacks: Malicious actors could potentially exploit cross-domain data to infer sensitive attributes or preferences about users, even if the data is anonymized.
Lack of Transparency and Control: Users might not be fully aware of how their data from different domains is being used for cross-domain recommendations. This lack of transparency and control over their data can erode trust.
Bias Concerns:

Amplification of Existing Biases: Cross-domain data might inherit and even amplify existing biases present in the individual domains. For instance, if a source domain exhibits gender bias in certain product recommendations, transferring this knowledge to another domain could perpetuate and exacerbate the bias.
Creation of New Biases: Combining data from different domains might inadvertently create new, unforeseen biases. For example, linking a user's preference for a particular genre of music with their purchase history for certain products could lead to biased recommendations based on spurious correlations.
Fairness and Discrimination: Biased recommendations can result in unfair or discriminatory outcomes for certain user groups, perpetuating existing societal inequalities.
Mitigating Ethical Concerns:

Privacy-Preserving Techniques: Employ techniques like differential privacy, federated learning, or homomorphic encryption to protect user privacy while enabling cross-domain recommendations.
Bias Detection and Mitigation: Develop and implement algorithms to detect and mitigate biases in both the data and the recommendation models. This includes techniques for fairness-aware learning and debiasing.
Transparency and User Control: Provide users with greater transparency into how their data is being used and offer them control over their data sharing preferences.
Ethical Guidelines and Regulations: Establish clear ethical guidelines and regulations for the responsible use of cross-domain data in recommendation systems.
Addressing these ethical implications is crucial to ensure that cross-domain recommendation systems are developed and deployed responsibly, respecting user privacy and promoting fairness.