Bootstrap Latent Representations for Multi-modal Recommendation (2207.05969v3)

Abstract: This paper studies the multi-modal recommendation problem, where the item multi-modality information (e.g., images and textual descriptions) is exploited to improve the recommendation accuracy. Besides the user-item interaction graph, existing state-of-the-art methods usually use auxiliary graphs (e.g., user-user or item-item relation graph) to augment the learned representations of users and/or items. These representations are often propagated and aggregated on auxiliary graphs using graph convolutional networks, which can be prohibitively expensive in computation and memory, especially for large graphs. Moreover, existing multi-modal recommendation methods usually leverage randomly sampled negative examples in Bayesian Personalized Ranking (BPR) loss to guide the learning of user/item representations, which increases the computational cost on large graphs and may also bring noisy supervision signals into the training process. To tackle the above issues, we propose a novel self-supervised multi-modal recommendation model, dubbed BM3, which requires neither augmentations from auxiliary graphs nor negative samples. Specifically, BM3 first bootstraps latent contrastive views from the representations of users and items with a simple dropout augmentation. It then jointly optimizes three multi-modal objectives to learn the representations of users and items by reconstructing the user-item interaction graph and aligning modality features under both inter- and intra-modality perspectives. BM3 alleviates both the need for contrasting with negative examples and the complex graph augmentation from an additional target network for contrastive view generation. We show BM3 outperforms prior recommendation models on three datasets with number of nodes ranging from 20K to 200K, while achieving a 2-9X reduction in training time. Our code is available at https://github.com/enoche/BM3.

Overview of "Bootstrap Latent Representations for Multi-modal Recommendation"

This paper addresses the challenges faced in multi-modal recommendation systems where item-related multi-modal information, such as images and textual descriptions, is leveraged to enhance recommendation accuracy. Traditional methods often rely on complex auxiliary graphs and negative sampling strategies, which can be computationally intensive and introduce noisy data. The research introduces BM3: a novel self-supervised multi-modal recommendation model that circumvents these issues by not utilizing auxiliary graphs or negative samples for learning.

Core Contributions and Methods

BM3 primarily distinguishes itself by employing self-supervised learning to create robust multi-modal representations without relying on computationally expensive procedures like auxiliary graph augmentations and negative sampling. This represents a significant shift from reliance on Bayesian Personalized Ranking (BPR) losses traditionally used in such systems.

Key Components of BM3:

1. Latent Representation Bootstrapping: BM3 generates latent contrastive views by applying dropout to user and item representations. This creates alternate views that are optimized without incurring the computational costs associated with auxiliary graphs.
2. Multi-modal Objectives: It combines three different objectives in its training regimen:
   • Reconstruction of the user-item interaction graph.
   • Alignment of features both inter-modality and intra-modality, which ensures that different modalities do not conflict but contribute to refined user/item representations.
3. Efficiency: BM3 achieves a significant reduction in training time (2-9 times faster) compared with existing models, which is particularly remarkable on large datasets.

Results and Implications

BM3 exhibits superior performance on datasets with varying nodes from 20K to 200K, outperforming existing state-of-the-art models. Its efficacy illustrates the potential of self-supervised learning frameworks in eliminating the need for negative sampling, which historically contributes to computational inefficiencies.

Practically, BM3 provides a framework that significantly reduces memory and computational costs, making it suitable for large-scale implementations in real-world applications. This is crucial for scenarios like e-commerce where systems need to scale rapidly without degradation in performance. Theoretically, this approach aligns with trends in self-supervised learning to create meaningful representations with minimal supervision and fewer dependencies on traditional graph structures.

Future Directions

The paper opens various avenues for further exploration. Extending the model to dynamically adapt dropout strategies or incorporating more diverse modalities could provide richer representations. Additionally, integrating BM3 with other existing recommendation systems frameworks could provide a holistic improvement across different aspects of recommendation accuracy and system efficiency. Future research could explore hybrid models that utilize both self-supervised and supervised methods to tackle complex recommendation scenarios.

In conclusion, BM3 is a significant contribution towards simplifying and enhancing multi-modal recommendation systems by leveraging the strengths of self-supervised learning, showcasing its potential in reducing computational complexity while maintaining or improving recommendation performance.