Due to the open nature of collaborative recommender systems, they can not effectively prevent malicious users from injecting fake profile data into the ratings database, which can significantly bias the system's output. With this problem in mind, in this paper we introduce the social trust of the users into the recommender system and build the trust relation between them. The values of trust among users are adjusted by using the reinforcement learning algorithm. On the basis of this, a user trust-based collaborative filtering recommendation algorithm is proposed. It uses the combined similarity to generate recommendation, which considers not only the similarity between user profiles but user trust as well. Experimental results show that the proposed algorithm outperforms the traditional user-based and item-based collaborative filtering algorithm in recommendation accuracy, especially in the face of malicious profile injection attacks.

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Vision Transformer (ViT) is emerging as a new leader in computer vision with its outstanding performance in many tasks (e.g., ImageNet-22k, JFT-300M). However, the success of ViT relies on pretraining on large datasets. It is difficult for us to use ViT to train from scratch on a small-scale imbalanced capsule endoscopic image dataset. This paper adopts a Transformer neural network with a spatial pooling configuration. Transfomer’s self-attention mechanism enables it to capture long-range information effectively, and the exploration of ViT spatial structure by pooling can further improve the performance of ViT on our small-scale capsule endoscopy dataset. We trained from scratch on two publicly available datasets for capsule endoscopy disease classification, obtained 79.15% accuracy on the multi-classification task of the Kvasir-Capsule dataset, and 98.63% accuracy on the binary classification task of the Red Lesion Endoscopy dataset.

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