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The traditional detection methods mainly rely on the artificial features, which are often extracted from one kind of user-generated information. In these methods, fine-grained interactions between users and items cannot be captured comprehensively, leading to the degradation of detection accuracy under various types of attacks. In this paper, we propose an ensemble detection method based on the automatic features extracted from multiple views. Firstly, to collaboratively discover the shilling profiles, the users\u2019 behaviors are analyzed from multiple views including ratings, item popularity, and user-user graph. Secondly, based on the data preprocessed from multiple views, the stacked denoising autoencoders are used to automatically extract user features with different corruption rates. Moreover, the features extracted from multiple views are effectively combined based on principal component analysis. Finally, according to the features extracted with different corruption rates, the weak classifiers are generated and then integrated to detect attacks. The experimental results on the MovieLens, Netflix, and Amazon datasets indicate that the proposed method can effectively detect various attacks.<\/jats:p>","DOI":"10.1155\/2019\/6523183","type":"journal-article","created":{"date-parts":[[2019,8,5]],"date-time":"2019-08-05T19:31:21Z","timestamp":1565033481000},"page":"1-13","source":"Crossref","is-referenced-by-count":23,"title":["Detecting Shilling Attacks with Automatic Features from Multiple Views"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7890-9001","authenticated-orcid":true,"given":"Yaojun","family":"Hao","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, Xinzhou Teachers University, Xinzhou, Shanxi Province, China"},{"name":"School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9595-3589","authenticated-orcid":true,"given":"Fuzhi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingshan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Xinzhou Teachers University, Xinzhou, Shanxi Province, 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