Triple Modality Fusion: Aligning Visual, Textual, and Graph Data with Large Language Models for Multi-Behavior Recommendations (2410.12228v2)

Abstract: Integrating diverse data modalities is crucial for enhancing the performance of personalized recommendation systems. Traditional models, which often rely on singular data sources, lack the depth needed to accurately capture the multifaceted nature of item features and user behaviors. This paper introduces a novel framework for multi-behavior recommendations, leveraging the fusion of triple-modality, which is visual, textual, and graph data through alignment with LLMs. By incorporating visual information, we capture contextual and aesthetic item characteristics; textual data provides insights into user interests and item features in detail; and graph data elucidates relationships within the item-behavior heterogeneous graphs. Our proposed model called Triple Modality Fusion (TMF) utilizes the power of LLMs to align and integrate these three modalities, achieving a comprehensive representation of user behaviors. The LLM models the user's interactions including behaviors and item features in natural languages. Initially, the LLM is warmed up using only natural language-based prompts. We then devise the modality fusion module based on cross-attention and self-attention mechanisms to integrate different modalities from other models into the same embedding space and incorporate them into an LLM. Extensive experiments demonstrate the effectiveness of our approach in improving recommendation accuracy. Further ablation studies validate the effectiveness of our model design and benefits of the TMF.

• The paper introduces the Triple Modality Fusion framework that aligns visual, textual, and graph modalities using large language models.
• It utilizes self-attention and cross-attention mechanisms to fuse data, achieving up to 38% improvement in HitRate@1 over baseline models.
• Experimental results on diverse datasets highlight TMF's potential to advance personalized recommendation systems and inspire future multi-modal integrations.

Triple Modality Fusion for Multi-Behavior Recommendations: An Expert Overview

The integration of multiple data modalities in recommendation systems has become a focal point in advancing personalized recommendations. The paper "Triple Modality Fusion: Aligning Visual, Textual, and Graph Data with LLMs for Multi-Behavior Recommendations" addresses the limitations of traditional recommendation models by introducing a framework that leverages triple modality fusion through LLMs.

Framework Overview

The proposed model, termed Triple Modality Fusion (TMF), aligns and integrates visual, textual, and graph modalities into a unified system. TMF aims to provide a comprehensive representation of user behaviors by effectively using the contextual and aesthetic features from images, detailed insights from textual descriptions, and relational data from graph structures. This integration is achieved through advanced alignment techniques that incorporate cross-attention and self-attention mechanisms, enabling the fusion of various modalities into a shared embedding space.

Methodology Details

The architecture employs a pre-trained LLM to serve as the backbone of the recommendation model. Initially, the LLM is primed using natural language prompts. It is then enhanced through a modality fusion module that aligns the different modalities. The module leverages self-attention to address dynamic importance within user behavior sequences and cross-attention to blend image and textual data, thus creating a rich, multi-modal representation.

Experimental Evaluation

The TMF framework's efficacy is validated through extensive experiments on three datasets representative of diverse categories: Electronics, Pets, and Sports. Results indicate significant improvements in recommendation accuracy over baseline models, both traditional sequence-based and LLM-based recommenders. Notably, TMF demonstrated superior performance by achieving up to 38% improvements in HitRate@1 over the best-performing baselines. This robustness highlights the framework's efficacy in managing complex user-item interaction types, as seen in datasets with varying item-to-user ratios.

Implications and Future Directions

TMF's introduction of a multi-modal approach has substantive implications for the development of multi-behavior recommendation systems. By successfully incorporating graph modalities into LLM-based systems, TMF sets a precedent for the potential incorporation of other data types—such as audio or interactive feedback—into recommendation engines.

The paper opens avenues for future research aimed at refining the modality fusion process and the integration of even larger, more dynamic datasets. Moreover, the utilization of LLMs in understanding complex interactions poses questions regarding scalability and the handling of evolving user behavior patterns.

Conclusion

The Triple Modality Fusion framework represents a pivotal step in integrating diverse data modalities within recommendation systems. By leveraging the strengths of LLMs, TMF provides a robust solution to capturing the multifaceted nature of user interactions and item characteristics. The demonstrated improvements in recommendation accuracy position TMF as a promising approach for advancing personalized recommendation systems. As AI continues to evolve, exploring deeper integrations of modality fusion could significantly enhance the personalization and accuracy of recommendations in various domains.