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AbstractShilling attacks have been a significant vulnerability of collaborative filtering (CF) recommender systems, and trust in CF recommender algorithms has been proven to be helpful for improving the accuracy of system recommendations. As a few studies have been devoted to trust in this area, we explore the benefits of using trust to resist shilling attacks. Rather than simply using user-generated trust values, we propose the genre trust degree, which differ in terms of the genres of itemsdoi:10.1007/s40747-021-00357-2 fatcat:r54hdarn3vfn7cbywbxuknadh4