Knowledge-Guided Cross-Domain Recommendation with Selective Information Transfer

Alapfogalmak

The core message of this paper is that not all information from the source domain is equally beneficial for cross-domain recommendation (CDR) tasks. The authors propose a novel framework, CoTrans, that selectively compresses and transfers relevant knowledge from the source domain to the target domain, guided by the target domain's information.

Kivonat

The paper proposes a novel cross-domain recommendation framework called CoTrans that addresses the challenge of indiscriminate knowledge transfer from the source domain to the target domain. The key ideas are:

1. Compression: CoTrans first compresses the source domain behaviors by selectively retaining the information that is relevant to the target domain, guided by the target domain's information. This is achieved by leveraging the graph information bottleneck theory.

2. Transfer: CoTrans then transfers the compressed and purified source domain information, along with the target domain information, to facilitate the recommendation task in the target domain. This ensures that only the most relevant and impactful knowledge is retained for the target domain recommendation.

3. Knowledge-enhanced Encoder: To overcome the challenge of non-overlapping items between the source and target domains, CoTrans employs a knowledge graph as an intermediary to bridge the content gap and enable effective knowledge propagation across domains.

Comprehensive experiments on three cross-domain datasets demonstrate that CoTrans significantly outperforms both single-domain and state-of-the-art cross-domain recommendation approaches.

Statisztikák

The paper reports the following key statistics:

The datasets used include Amazon Movies (AM), Amazon Books (AB), and Amazon CDs (AC).
The number of users ranges from 4,933 to 52,625, and the number of items ranges from 16,100 to 179,743 across the datasets.
The training set sizes range from 98,763 to 139,893 interactions.

Idézetek

"To alleviate information overload on the web and understand user preferences, recommender systems have become the key component for online marketplaces and achieved great success."
"Unfortunately, it may not hold in most scenarios, especially for complex real-world applications, as interactions (e.g., click or purchase) of a certain user towards items are not completely motivated by his/her interests."

Főbb Kivonatok

The Devil is in the Sources! Knowledge Enhanced Cross-Domain Recommendation in an Information Bottleneck Perspective

by Binbin Hu, W... : arxiv.org 10-01-2024

https://arxiv.org/pdf/2409.19574.pdf

Mélyebb kérdések

How can the proposed CoTrans framework be extended to handle dynamic changes in user preferences and item catalogs across domains over time?

The CoTrans framework can be extended to accommodate dynamic changes in user preferences and item catalogs by incorporating a temporal dimension into its architecture. This can be achieved through the following strategies:

Temporal Graph Representation: Instead of static user-item interaction graphs, a temporal graph can be constructed where edges represent interactions over time. This allows the model to capture the evolution of user preferences and item popularity, enabling it to adapt recommendations based on recent behaviors.

Incremental Learning: Implementing incremental learning techniques can help the CoTrans framework update its knowledge base without retraining from scratch. By continuously integrating new user interactions and item information, the model can maintain its relevance and accuracy in recommendations.

Dynamic Knowledge Graphs: Utilizing dynamic knowledge graphs that evolve with user interactions and item updates can enhance the framework's ability to understand the changing relationships between items across domains. This can involve real-time updates to the knowledge graph based on user feedback and item catalog changes.

Feedback Loops: Incorporating user feedback loops can help the model learn from user interactions in real-time. By analyzing user responses to recommendations, the framework can adjust its compression and transfer mechanisms to better align with shifting preferences.

Adaptive Compression and Transfer Mechanisms: The compression and transfer processes can be made adaptive by integrating mechanisms that assess the relevance of source domain knowledge based on temporal factors. This could involve weighting the importance of historical interactions differently based on their recency.

By implementing these strategies, the CoTrans framework can effectively manage the dynamic nature of user preferences and item catalogs, ensuring that recommendations remain relevant and personalized over time.

What are the potential limitations of the graph information bottleneck approach used in CoTrans, and how can it be further improved to better capture the complex relationships between source and target domains?

While the graph information bottleneck (GIB) approach in CoTrans offers a robust framework for selective knowledge transfer, it does have potential limitations:

Over-Simplification of Relationships: The GIB approach may oversimplify the complex relationships between source and target domains by focusing primarily on mutual information. This could lead to the loss of nuanced interactions that are critical for accurate recommendations.

Dependency on Quality of Input Data: The effectiveness of the GIB is heavily reliant on the quality and completeness of the input data. In scenarios where user interactions are sparse or noisy, the model may struggle to identify relevant behaviors, leading to suboptimal recommendations.

Scalability Issues: As the number of users and items increases, the computational complexity of maintaining and updating the graph information bottleneck can become a bottleneck itself, potentially hindering real-time performance.

Limited Contextual Awareness: The current formulation may not fully account for contextual factors that influence user preferences, such as time, location, or social influences. This lack of contextual awareness can limit the model's ability to adapt to varying user needs.

To improve the GIB approach, the following enhancements can be considered:

Incorporating Contextual Information: By integrating contextual features into the GIB framework, the model can better capture the situational factors that influence user preferences, leading to more personalized recommendations.

Multi-View Learning: Implementing multi-view learning techniques can help the model leverage different perspectives of user behavior and item characteristics, enriching the representation of relationships between domains.

Hierarchical Graph Structures: Utilizing hierarchical graph structures can allow for a more granular representation of relationships, enabling the model to capture both high-level trends and low-level interactions effectively.

Regularization Techniques: Applying regularization techniques can help mitigate overfitting and improve the model's generalization capabilities, particularly in scenarios with limited data.
By addressing these limitations and incorporating these improvements, the GIB approach can enhance its ability to capture the complex relationships between source and target domains, ultimately leading to better performance in cross-domain recommendation tasks.

Given the success of CoTrans in cross-domain recommendation, how can the principles of selective knowledge transfer and domain-aware compression be applied to other multi-task learning or transfer learning problems beyond recommender systems?

The principles of selective knowledge transfer and domain-aware compression demonstrated in CoTrans can be effectively applied to various multi-task learning and transfer learning problems beyond recommender systems in the following ways:

Selective Knowledge Transfer in Natural Language Processing (NLP): In NLP tasks, models often need to transfer knowledge from one language to another or from one domain (e.g., sentiment analysis) to another (e.g., topic classification). By employing selective knowledge transfer, models can focus on relevant linguistic features and contextual information that are beneficial for the target task, improving performance in low-resource languages or domains.

Domain-Aware Compression in Computer Vision: In computer vision, models trained on large datasets may need to adapt to specific tasks with limited data, such as medical image analysis. Domain-aware compression can help identify and retain the most relevant features from the source domain (e.g., general object recognition) while discarding irrelevant information, thus enhancing the model's ability to generalize to the target domain.

Selective Transfer in Healthcare Applications: In healthcare, predictive models often need to transfer knowledge from one patient population to another. By applying selective knowledge transfer, models can focus on patient characteristics and treatment responses that are most relevant to the new population, leading to more accurate predictions and personalized treatment plans.

Adaptive Learning in Robotics: In robotics, selective knowledge transfer can be utilized to adapt robotic behaviors learned in one environment to new, unstructured environments. By compressing and transferring only the relevant skills and experiences, robots can quickly adapt to new tasks without extensive retraining.

Multi-Task Learning in Finance: In financial applications, models often need to perform multiple tasks, such as fraud detection and credit scoring. By employing domain-aware compression, the model can prioritize features that are relevant across tasks, improving overall performance and efficiency.

By leveraging the principles of selective knowledge transfer and domain-aware compression, various fields can enhance their models' adaptability and performance, leading to more effective solutions in multi-task and transfer learning scenarios.