CIRP: A Novel Cross-Item Relational Pre-training Framework for Multimodal Product Bundling

Conceitos essenciais

The proposed CIRP framework integrates cross-item relational information into a multimodal pre-trained model to enhance product bundling performance, while preserving the in-depth aligned multimodal semantics.

Resumo

The paper presents a novel framework called Cross-Item Relational Pre-training (CIRP) for item representation learning in product bundling. The key highlights are:

CIRP employs a multimodal encoder to generate image and text representations for items. It leverages both the cross-item contrastive (CIC) loss and individual item's image-text contrastive (ITC) loss as the pre-train objectives.

The CIC loss aims to integrate cross-item relation modeling capability into the multimodal encoder, while the ITC loss preserves the in-depth aligned multimodal semantics. This allows CIRP to generate relation-aware multimodal representations even for cold-start items.

To eliminate potential noise and reduce computational cost, CIRP introduces a relation pruning module to remove noisy and redundant relations from the item-item graph.

Experiments on three e-commerce datasets demonstrate that CIRP outperforms various leading representation learning methods for the downstream task of product bundling. The relation pruning module can significantly improve pre-training efficiency with marginal performance drop.

Further analysis shows that CIRP can effectively capture the cross-item relations and maintain the multimodal semantic alignment, leading to superior item representations for product bundling.

Estatísticas

The co-purchase relations between items are used to construct the item-item graph.
The datasets contain 469,153 items and 1,023,078 item-item relations in total for pre-training.
For the downstream task of product bundling, there are 11,753 bundles with an average size of 3.47 items.

Citações

"Even for cold-start items that have no relations, their representations are still relation-aware."
"Experiments on three large-scale e-commerce show that CIRP can significantly boost the performance of product bundling compared with various leading methods for item representation learning."
"When pruning 90% of the relations, our method only experiences a slight performance drop, while just taking 1/10 of the pre-training time."

Principais Insights Extraídos De

CIRP

by Yunshan Ma,Y... às arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01735.pdf

Perguntas Mais Profundas

How can the proposed CIRP framework be extended to incorporate other types of item-item relations beyond co-purchase, such as sequential interactions or knowledge graph relations?

In order to incorporate other types of item-item relations into the CIRP framework, such as sequential interactions or knowledge graph relations, several modifications and additions can be made.

Sequential Interactions: To include sequential interactions between items, the item-item relation graph can be expanded to capture the order in which items are interacted with. This sequential information can be encoded in the graph structure, and the pre-training objectives can be adjusted to consider the sequential nature of these interactions.

Knowledge Graph Relations: For incorporating knowledge graph relations, additional data sources or external knowledge bases can be integrated into the pre-training process. By leveraging existing knowledge graphs or creating domain-specific knowledge graphs, the model can learn to capture complex relationships between items based on external information.

Multi-relational Graphs: The item-item relation graph can be extended to support multiple types of relations, allowing the model to learn from diverse sources of information. Different relation types can be encoded as different edge types in the graph, enabling the model to capture a wide range of relationships between items.

By extending the CIRP framework to incorporate various types of item-item relations, the model can become more versatile and capable of capturing the complex interactions and dependencies present in real-world scenarios.

How can the potential limitations of the current CIRP framework be further improved to handle more complex product bundling scenarios?

While the CIRP framework shows promising results in product bundling, there are potential limitations that can be addressed to handle more complex scenarios:

Sparse Data Handling: To handle sparse data and cold-start items more effectively, techniques such as transfer learning or data augmentation can be employed. By leveraging information from related domains or generating synthetic data, the model can improve its performance on sparse or unseen items.

Dynamic Graph Construction: Instead of relying solely on co-purchase data for graph construction, dynamic graph construction methods can be implemented to adapt to changing relationships between items. This can involve incorporating temporal information or user feedback to update the item-item relation graph.

Multi-modal Fusion: Enhancing the fusion of multimodal features can improve the model's ability to capture complex relationships between items. Techniques like attention mechanisms or graph neural networks can be utilized to integrate image, text, and relational data more effectively.

Interpretable Representations: Developing methods to interpret and visualize the learned representations can provide insights into how the model makes bundling decisions. This can help in understanding the reasoning behind the model's recommendations and improve transparency in decision-making.

By addressing these limitations and incorporating advanced techniques, the CIRP framework can be further improved to handle more complex product bundling scenarios with enhanced performance and robustness.

Given the success of CIRP in product bundling, how can the cross-item relational pre-training concept be applied to other multimodal applications beyond e-commerce, such as multimedia recommendation or cross-modal retrieval?

The concept of cross-item relational pre-training demonstrated in CIRP can be extended to various other multimodal applications beyond e-commerce, such as multimedia recommendation and cross-modal retrieval. Here are some ways to apply this concept in different domains:

Multimedia Recommendation: In multimedia recommendation systems, the cross-item relational pre-training can be used to learn representations that capture relationships between different types of media content, such as images, videos, and audio. By pre-training on multimodal data with relational information, the model can provide more personalized and context-aware recommendations.

Cross-Modal Retrieval: For cross-modal retrieval tasks, where the goal is to retrieve relevant information across different modalities, the pre-trained model can learn to align and match features from diverse sources. By incorporating cross-item relations, the model can better understand the connections between different types of data and improve retrieval accuracy.

Healthcare Applications: In healthcare, the concept of cross-item relational pre-training can be applied to multimodal data sources like patient records, medical images, and clinical notes. By learning relationships between different healthcare data modalities, the model can assist in diagnosis, treatment planning, and patient care.

Smart Cities and IoT: For smart city applications and Internet of Things (IoT) systems, the pre-trained model can learn to understand the interactions and dependencies between various sensors, devices, and environmental data. This can enable more efficient resource management, predictive maintenance, and urban planning.

By adapting the cross-item relational pre-training concept to different multimodal applications, the model can leverage the power of relational information to enhance performance, improve generalization, and provide more accurate and context-aware recommendations or retrievals.