Knowledge-Aware Multi-Intent Contrastive Learning for Enhancing Multi-Behavior Recommendation

Temel Kavramlar

Leveraging knowledge graphs and contrastive learning to effectively capture user intents across different behaviors and address data sparsity issues for improved multi-behavior recommendation.

Özet

The paper proposes a novel framework called Knowledge-Aware Multi-Intent Contrastive Learning (KAMCL) for multi-behavior recommendation. The key highlights are:

1. KAMCL uses relationships in the knowledge graph to construct user intents, aiming to mine the connections between users' multi-behaviors from the perspective of intents to achieve more accurate recommendations.

2. KAMCL is equipped with two contrastive learning schemes - relation-aware item contrastive learning and behavior-aware user contrastive learning. These schemes help alleviate the data scarcity problem and further enhance user and item representations.

3. The relation-aware knowledge graph aggregation module extracts semantic information about items from different perspectives by partitioning the knowledge graph based on relations.

4. The intent generation module leverages the relational information in the knowledge graph to capture user intent information across different behaviors.

5. The intent-based multi-behavior interaction module explores the connections between behaviors from the perspective of user intent.

6. Extensive experiments on three real-world datasets demonstrate the superiority of KAMCL over state-of-the-art multi-behavior recommendation methods.

İstatistikler

Users tend to pay greater attention to information such as ratings and brands when viewing products, but become more price-conscious when making purchases.
The acquisition of target behaviors (e.g., purchase) can be expensive, leading to data sparsity problems, while capturing auxiliary behaviors (e.g., view, add to cart) is cost-effective.

Alıntılar

"Users' interactions with items under different behaviors are driven by distinct intents."
"The exploration of correlations between behaviors from the intent perspective is often overlooked in current approaches."
"The key challenge lies in extracting meaningful preference information from these behaviors."

Önemli Bilgiler Şuradan Elde Edildi

Knowledge-Aware Multi-Intent Contrastive Learning for Multi-Behavior Recommendation

by Shunpan Lian... : arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.11993.pdf

Daha Derin Sorular

How can the proposed KAMCL framework be extended to incorporate additional user and item features beyond the knowledge graph to further enhance recommendation performance?

The KAMCL framework can be extended to incorporate additional user and item features by integrating external data sources and utilizing advanced feature engineering techniques. Here are some ways to enhance the recommendation performance:

Incorporating User Demographics: By including demographic information such as age, gender, location, and preferences, the model can better understand user behavior and tailor recommendations accordingly. This data can be sourced from user profiles or external databases.

Utilizing User Interaction History: Incorporating the sequential behavior of users, such as click sequences, browsing patterns, and time-based interactions, can provide valuable insights into user preferences and intent. Recurrent Neural Networks (RNNs) or Transformer models can be employed to capture sequential patterns.

Feature Engineering: Creating new features based on user-item interactions, such as frequency of interactions, recency of interactions, and diversity of items interacted with, can enrich the input data and improve recommendation accuracy.

Textual Data Processing: If available, textual data such as reviews, product descriptions, and user comments can be processed using Natural Language Processing (NLP) techniques to extract valuable information for recommendation purposes.

Social Network Analysis: Leveraging social network connections and user relationships can enhance the understanding of user preferences and influence in the recommendation process. Graph-based algorithms can be used to analyze social network structures.

By incorporating these additional user and item features alongside the knowledge graph, the KAMCL framework can create a more comprehensive and personalized recommendation system.

How can the proposed KAMCL framework be extended to incorporate additional user and item features beyond the knowledge graph to further enhance recommendation performance?

The intent-based approach in the KAMCL framework has certain limitations that can impact its effectiveness in handling complex user behavior patterns. Here are some potential limitations and suggestions for improvement:

Limited Intent Representation: The current intent generation module may oversimplify user intents by relying solely on knowledge graph relationships. To handle more complex user behavior patterns, the model can be enhanced by incorporating user feedback, explicit intent labeling, or user surveys to capture diverse user intents accurately.

Intent Ambiguity: Users may exhibit multiple intents simultaneously, leading to ambiguity in intent representation. To address this, the model can be extended to support multi-label intent classification or probabilistic intent modeling to capture the uncertainty in user intentions.

Scalability: As the number of intents and behaviors increases, the model's scalability may become a concern. Implementing efficient intent clustering techniques or hierarchical intent modeling can help manage a large number of intents and behaviors effectively.

Contextual Information: Incorporating contextual information such as user context, session context, and environmental factors can provide a deeper understanding of user behavior. Context-aware intent modeling can improve the model's ability to adapt to varying user contexts.

Evaluation Metrics: The evaluation of intent-based recommendation systems may require specialized metrics to assess the quality of intent representation and recommendation accuracy. Developing custom evaluation metrics tailored to intent-based approaches can provide more insightful performance analysis.

By addressing these limitations and incorporating advanced techniques for intent modeling and representation, the KAMCL framework can be improved to handle more complex user behavior patterns effectively.

How can the KAMCL framework be adapted to other recommendation scenarios beyond multi-behavior, such as cross-domain or sequential recommendations?

The KAMCL framework can be adapted to other recommendation scenarios by modifying its architecture and components to suit the specific requirements of different recommendation tasks. Here's how it can be adapted for cross-domain and sequential recommendations:

Cross-Domain Recommendations:

Feature Alignment: For cross-domain recommendations, aligning user and item features across different domains is crucial. The model can incorporate domain adaptation techniques to learn shared representations and adapt to diverse domains.
Domain-specific Knowledge Graphs: Create domain-specific knowledge graphs to capture domain-specific relationships and attributes. The model can leverage multiple knowledge graphs to enhance cross-domain recommendation accuracy.

Sequential Recommendations:

Temporal Modeling: For sequential recommendations, the model needs to capture temporal dynamics and user behavior evolution over time. Recurrent Neural Networks (RNNs) or Transformer models can be used to model sequential patterns and predict future user actions.
Session-based Recommendations: Implement session-based recommendation strategies to consider user interactions within a session. The model can focus on short-term user preferences and adapt recommendations based on session context.

Hybrid Recommendations:

Combining Approaches: To handle diverse recommendation scenarios, a hybrid approach combining collaborative filtering, content-based filtering, and knowledge graph embedding techniques can be employed. The model can adapt its components based on the characteristics of the recommendation task.

Evaluation Metrics:

Task-specific Metrics: Define task-specific evaluation metrics for cross-domain and sequential recommendations to measure the model's performance accurately. Metrics like cross-domain accuracy, sequential recommendation precision, and recall can provide insights into the model's effectiveness.

By customizing the KAMCL framework with domain-specific features, knowledge graphs, and modeling techniques, it can be adapted to various recommendation scenarios beyond multi-behavior, including cross-domain and sequential recommendations.