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Collaborative filtering recommendation using fusing criteria against shilling attacks.

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Title: Collaborative filtering recommendation using fusing criteria against shilling attacks.
Authors: Li, Li (AUTHOR), Wang, Zhongqun (AUTHOR), Li, Chen (AUTHOR), Chen, Linjun (AUTHOR), Wang, Yong (AUTHOR)
Source: Connection Science. Dec2022, Vol. 34 Issue 1, p1678-1696. 19p.
Subjects: Tags (Metadata), Recommender systems, Information resources, Reliability in engineering, Dynamic models
Abstract: The collaborative filtering recommendation technique (CFR) is one of the techniques used in recommended systems, in which the most proximal neighbours to a target user are selected. Their profiles are used to predict rating for items as yet unrated by that target user. However, malicious users inject fake user profiles to destroy the security and reliability of the recommender systems, which is called shilling attacks. Therefore, it is crucial to improve the recommendation technique against shilling attacks. Malicious users use a single method to perform shilling attacks. Intuitively, fusing multiple criteria to construct CFR can effectively resist shilling attacks. A novel CFR is proposed against shilling attacks (called CFR-F). In our approach, a similar interest users' resource set is obtained first by integrating users' dynamic interest model and social tags. Then, a similar interest user resource set is selected according to a strategy that selects preference influence weight based on user background. Our experimental results show that our approach can recommend accurate information resources and has a lower Mean Absolute Error (MAE) and Average Prediction Shift (APS) than traditional techniques by 50% and 20%, respectively. [ABSTRACT FROM AUTHOR]
Copyright of Connection Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Collaborative filtering recommendation using fusing criteria against shilling attacks.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Li%22">Li, Li</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Zhongqun%22">Wang, Zhongqun</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Chen%22">Li, Chen</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Linjun%22">Chen, Linjun</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Yong%22">Wang, Yong</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Connection+Science%22">Connection Science</searchLink>. Dec2022, Vol. 34 Issue 1, p1678-1696. 19p.
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  Label: Abstract
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  Data: The collaborative filtering recommendation technique (CFR) is one of the techniques used in recommended systems, in which the most proximal neighbours to a target user are selected. Their profiles are used to predict rating for items as yet unrated by that target user. However, malicious users inject fake user profiles to destroy the security and reliability of the recommender systems, which is called shilling attacks. Therefore, it is crucial to improve the recommendation technique against shilling attacks. Malicious users use a single method to perform shilling attacks. Intuitively, fusing multiple criteria to construct CFR can effectively resist shilling attacks. A novel CFR is proposed against shilling attacks (called CFR-F). In our approach, a similar interest users' resource set is obtained first by integrating users' dynamic interest model and social tags. Then, a similar interest user resource set is selected according to a strategy that selects preference influence weight based on user background. Our experimental results show that our approach can recommend accurate information resources and has a lower Mean Absolute Error (MAE) and Average Prediction Shift (APS) than traditional techniques by 50% and 20%, respectively. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Connection Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/09540091.2022.2078280
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        Text: English
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              Text: Dec2022
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