A Unified Framework for Cross-Domain Recommendation: Enhancing Prediction Performance by Leveraging Knowledge Across Domains

Khái niệm cốt lõi

A unified framework, UniCDR+, is proposed to effectively transfer knowledge across multiple domains and adapt to various cross-domain recommendation scenarios, outperforming existing task-specific approaches.

Tóm tắt

The paper presents a unified framework, UniCDR+, for cross-domain recommendation (CDR) that aims to enhance prediction performance by leveraging knowledge from related source domains.

Key highlights:

1. CDR is an effective approach to address data sparsity and cold-start issues in domain-specific recommender systems by transferring knowledge across domains.
2. Existing CDR methods are often tailored to specific vertical scenarios and lack the capacity to adapt to multiple horizontal scenarios.
3. UniCDR+ extends the previous SOTA model UniCDR by incorporating several key components:
   • User/item feature engineering to leverage rich contextual information
   • Item-level graph neural network to model multi-hop item collaborative signals
   • Interaction sequence aggregators to capture dynamic user preferences
   • Soft contrastive objective to enhance domain-invariant representation learning
   • Multi-behavior prediction to handle diverse user actions
4. UniCDR+ demonstrates strong performance on 5 public CDR datasets and has been successfully deployed in the Kuaishou Living-Room recommendation service, outperforming the base DLRM model.
5. Experiments show UniCDR+'s unique transfer ability across different interaction types and downstream tasks, including intra-domain and inter-domain recommendation.

Thống kê

The Short-Video domain data is more than 20x that of the Living-Room domain, leading to data sparsity and cold-start issues in the Living-Room recommender system.
The Kuaishou Living-Room recommender system handles billions of user requests per day.
UniCDR+ achieved 0.10% improvement in offline AUC and GAUC metrics compared to the base DLRM model.
Online A/B testing of UniCDR+ showed 0.6% increase in Click, 0.7% increase in Long-View, 1.1% increase in Follow the author, and 1.0% increase in Watch-Time for the Living-Room service.

Trích dẫn

"In addressing the persistent challenges of data-sparsity and cold-start issues in domain-expert recommender systems, Cross-Domain Recommendation (CDR) emerges as a promising methodology."
"With so many different CDR combination scenario settings, the proposed scenario-expert approaches are tailored to address a specific vertical CDR scenario, and often lack the capacity to adapt to multiple horizontal scenarios."
"Compared with task-expertise methods such as DisenCDR, CDRIB, M3Rec and C2DSR, our UniCDR+ outperform them and shows unique robustness in different Scenarios."

Thông tin chi tiết chính được chắt lọc từ

A Unified Framework for Cross-Domain Recommendation

by Jiangxia Cao... lúc arxiv.org 09-10-2024

https://arxiv.org/pdf/2409.04540.pdf

Yêu cầu sâu hơn

How can the proposed UniCDR+ framework be extended to handle more complex cross-domain scenarios, such as those involving multi-modal data or dynamic user-item interactions?

The UniCDR+ framework can be extended to accommodate more complex cross-domain scenarios by integrating multi-modal data and enhancing its capability to model dynamic user-item interactions.

Incorporating Multi-Modal Data: To handle multi-modal data, such as text, images, and videos, the framework can be enhanced by adding specialized embedding modules for each data type. For instance, convolutional neural networks (CNNs) can be employed for image data, while recurrent neural networks (RNNs) or transformers can be utilized for text data. By creating a unified representation that combines these embeddings, the model can leverage the rich information contained in different modalities, thus improving the recommendation quality.

Dynamic User-Item Interactions: To capture the dynamic nature of user-item interactions, the framework can implement recurrent architectures or attention mechanisms that account for the temporal aspects of user behavior. For example, using Long Short-Term Memory (LSTM) networks or attention-based models can help in understanding how user preferences evolve over time. Additionally, incorporating real-time feedback loops can allow the model to adapt to changes in user behavior instantaneously, thus enhancing the relevance of recommendations.

Multi-Hop Interaction Modeling: The framework can also be extended to include multi-hop interaction modeling, where the relationships between items are explored beyond direct interactions. This can be achieved through graph neural networks (GNNs) that capture the complex relationships between items across different domains, allowing for a more nuanced understanding of user preferences.

By integrating these enhancements, the UniCDR+ framework can effectively address the challenges posed by complex cross-domain scenarios, leading to more accurate and personalized recommendations.

What are the potential limitations of the domain-invariant representation learning approach used in UniCDR+, and how could it be further improved to better capture domain-specific nuances?

The domain-invariant representation learning approach in UniCDR+ has several potential limitations:

Loss of Domain-Specific Information: While the goal of domain-invariant learning is to create a unified representation that generalizes across domains, this can lead to the loss of critical domain-specific nuances. For instance, user preferences may vary significantly between domains, and a single representation may not adequately capture these differences.

Over-Smoothing of Representations: The process of aligning representations across domains can result in over-smoothing, where the unique characteristics of individual domains are diminished. This can hinder the model's ability to make accurate predictions, especially in scenarios where domain-specific features are crucial for understanding user behavior.

Inflexibility to Domain Changes: The static nature of the learned representations may not adapt well to changes in domain characteristics over time. For example, if a new trend emerges in one domain, the model may struggle to incorporate this information without retraining.

To improve the domain-invariant representation learning approach, the following strategies could be implemented:

Hierarchical Representation Learning: By adopting a hierarchical approach that allows for both shared and domain-specific representations, the model can maintain the benefits of domain invariance while still capturing essential domain-specific features. This could involve creating separate layers for domain-specific embeddings that feed into a shared representation layer.

Adaptive Learning Mechanisms: Implementing adaptive learning techniques that allow the model to adjust its representations based on real-time feedback and changes in user behavior can enhance its responsiveness to domain-specific nuances. Techniques such as meta-learning or continual learning could be explored to facilitate this adaptability.

Incorporating Attention Mechanisms: Utilizing attention mechanisms can help the model focus on the most relevant features for each domain, thereby preserving important domain-specific information while still benefiting from shared knowledge.

By addressing these limitations, the UniCDR+ framework can enhance its ability to capture the complexities of user preferences across different domains, leading to improved recommendation performance.

Given the success of UniCDR+ in the Kuaishou Living-Room recommendation service, how could the framework be adapted to address challenges in other real-world recommender systems, such as those in e-commerce or social media platforms?

The success of UniCDR+ in the Kuaishou Living-Room recommendation service provides valuable insights that can be adapted to other real-world recommender systems, such as e-commerce and social media platforms. Here are several strategies for adaptation:

Customization for Domain-Specific Features: In e-commerce, user preferences are often influenced by product attributes, pricing, and promotional offers. The UniCDR+ framework can be tailored to incorporate specific features relevant to e-commerce, such as user purchase history, product reviews, and seasonal trends. By enhancing the feature engineering module to include these attributes, the model can provide more relevant recommendations.

Social Interaction Modeling: For social media platforms, user interactions are often driven by social connections and content engagement. The framework can be adapted to include social graph information, allowing the model to leverage relationships between users to enhance recommendations. This could involve integrating social network analysis techniques to identify influential users and trending content.

Real-Time Data Processing: E-commerce and social media platforms require real-time processing of user interactions to remain relevant. The UniCDR+ framework can be enhanced with streaming data processing capabilities, enabling it to update recommendations based on the latest user behavior and interactions. This could involve implementing online learning algorithms that continuously refine the model as new data comes in.

Multi-Objective Optimization: In e-commerce, the goals of maximizing sales and enhancing user satisfaction may conflict. The framework can be adapted to optimize multiple objectives simultaneously, such as balancing between promoting high-margin products and ensuring user satisfaction. This could involve using multi-objective optimization techniques to find a suitable trade-off.

Cross-Domain Recommendations: The ability to leverage user behavior across different domains (e.g., from browsing to purchasing) can be particularly beneficial in e-commerce. The UniCDR+ framework can be adapted to facilitate cross-domain recommendations, where insights from one domain (e.g., product browsing) inform recommendations in another (e.g., product purchasing).

By implementing these adaptations, the UniCDR+ framework can effectively address the unique challenges faced by various real-world recommender systems, enhancing its applicability and performance across different domains.