With the development of social networks, the research of integrated social information recommendation models has received extensive attention. However, most existing social recommendation models are based on the matrix factorization technique which ignore the impact of the relationships between items on users' interests, resulting in a decline of recommendation accuracy. To solve this problem, this paper proposes a double regularization matrix factorization recommendation algorithm. The algorithm first uses attribute information and manifold learning to calculate similarity. Then, the matrix factorization model is constrained through the regularization of item association relations and user social relations. Experimental results on real datasets show that the proposed method can effectively alleviate problems such as cold start and data sparsity in the recommender system and improve the recommendation accuracy compared with those of existing methods.

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Current website defacement detection methods often ignore security and credibility in the detection process. Furthermore, with the gradual development of dynamic websites, false positives and underreports of website defacement have periodically occurred. Therefore, to enhance the credibility of website defacement detection and reduce the false-positive rate and the false-negative rate of website defacement, this paper proposes a fine-grained trust detection scheme called WebTD, that combines machine learning and blockchain. WebTD consists of two parts: an analysis layer and a verification layer. The analysis layer is the key to improving the success rate of website defacement detection. This layer mainly uses the naive Bayes (NB) algorithm to decouple and segment different types of web page content, and then preprocess the segmented data to establish a complete analysis model. Second, the verification layer is the key to establishing a credible detection mechanism. WebTD develops a new blockchain model and proposes a multi-value verification algorithm to achieve a multilayer detection mechanism for the blockchain. In addition, to quickly locate and repair the defaced data of the website, the Merkle tree (MT) algorithm is used to calculate the preprocessed data. Finally, we evaluate WebTD against two state-of-the-art research schemes. The experimental results and the security analysis show that WebTD not only establishes a credible web service detection mechanism but also keeps the detection success rate above 98%, which can effectively ensure the integrity of the website.

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