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 2-9 × reduction in training time. Code implementation is located at: https://github.com/enoche/BM3.

This paper presents an open-source toolbox, MMRec for multimodal recommendation. MMRec simplifies and canonicalizes the process of implementing and comparing multimodal recommendation models. The objective of MMRec is to provide a unified and configurable arena that can minimize the effort in implementing and testing multimodal recommendation models. It enables multimodal models, ranging from traditional matrix factorization to modern graph-based algorithms, capable of fusing information from multiple modalities simultaneously. Our documentation, examples, and source code are available at https://github.com/enoche/MMRec.

Collaborative filtering (CF) is widely used to learn informative latent representations of users and items from observed interactions. Existing CF-based methods commonly adopt negative sampling to discriminate different items. That is, observed user-item pairs are treated as positive instances; unobserved pairs are considered as negative instances and are sampled under a defined distribution for training. Training with negative sampling on large datasets is computationally expensive. Further, negative items should be carefully sampled under the defined distribution, in order to avoid selecting an observed positive item in the training dataset. Unavoidably, some negative items sampled from the training dataset could be positive in the test set. Recently, self-supervised learning (SSL) , has emerged as a powerful tool to learn a model without negative samples. In this paper, we propose a self-supervised collaborative filtering framework (SelfCF) , that is specially designed for recommender scenario with implicit feedback. The proposed SelfCF framework simplifies Siamese networks and can be easily applied to existing deep-learning based CF models, which we refer to as backbone networks. The main idea of SelfCF is to augment the latent embeddings generated by backbone networks instead of the raw input of user/item ids. We propose and study three embedding perturbation techniques that can be applied to different types of backbone networks including both traditional CF models and graph-based models. The framework enables learning informative representations of users and items without negative samples, and is agnostic to the encapsulated backbones. We conduct experimental comparisons on four datasets, one self-supervised framework, and eight baselines to show that our framework may achieve even better recommendation accuracy than the encapsulated supervised counterpart with a 2×–4× faster training speed. The results also demonstrate that SelfCF can boost up the accuracy of a self-supervised framework BUIR by 17.79% on average and shows competitive performance with baselines.

User-item interaction data in recommender systems is a form of dyadic relation, reflecting user preferences for specific items. To generate accurate recommendations, it is crucial to learn representations for both users and items. Recent multimodal recommendation models achieve higher accuracy by incorporating multimodal features, such as images and text descriptions. However, our experimental findings reveal that current multimodality fusion methods employed in state-of-the-art models may adversely affect recommendation performance without compromising model architectures. Moreover, these models seldom investigate internal relations between item-item and user-user interactions. In light of these findings, we propose a model that enhances the dyadic relations by learning Dual RepresentAtions of both users and items via constructing homogeneous Graphs for multimOdal recommeNdation. We name our model as DRAGON. Specifically, DRAGON constructs user-user graphs based on commonly interacted items and item-item graphs derived from item multimodal features. Graph learning on both the user-item heterogeneous and homogeneous graphs is used to obtain dual representations of users and items. To capture information from each modality, DRAGON employs an effective fusion method, attentive concatenation. Extensive experiments on three public datasets and eight baselines show that DRAGON can outperform the strongest baseline by 21.41% on average. Our code is available at https://github.com/hongyurain/DRAGON.

IoT data exchange plays a vital role in supporting various applications and services with massive IoT devices. However, the existence of malicious devices threatens the integrity and reliability of the exchanged data. Trust management has been used to mitigate the impact of malicious devices in centralized and decentralized architectures. However, most of these traditional trust management systems bear computation, storage, and communication challenges. In this study, we propose a semi-centralized trust management system architecture based on blockchain in both single and multiple domains. The IoT devices are centralized organized by cloud servers who coordinately sustain a rating data ledger within each domain based the proposed rotation based consensus protocol in a decentralized manner to support cross-domain data exchange. A computational trust model is proposed by aggregating the direct and indirect trust information, where we elaborately design decay function, recommendation credibility and adaptable weights so as to calculate the trust value of dynamic malicious devices. Finally, we evaluate the proposed system model in various situations through simulation based experiments and compare it with two classical models in the literature. The experimental results demonstrate the effectiveness of the proposed trust model in identifying malicious devices and mitigating the influence of malicious devices.