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| Robust Recommendation Algorithm Based on the Identification of Suspicious Users and Matrix Factorization |
| Huawei Yi, Fuzhi Zhang |
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Abstract The existing recommendation algorithms have lower robustness against
shilling attacks. With this in mind, in this paper we propose a
robust recommendation algorithm based on the identification of
suspicious users and matrix factorization. We first give the
computational methods of Deviation Degree about the Number of
Ratings (DDNR) and Average Similarity of Neighbors (ASN) according
to the distribution of users' ratings. Then we identify suspicious
users based on the differences of computational results of users'
DDNR and ASN. Finally, we introduce the matrix factorization
technology which incorporates indicator function and the
identification results of suspicious users to make recommendations
for users. Experimental results show that the proposed algorithm not
only improves the recommendation accuracy, but also has better
robustness.
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| DOI:10.12733/jics20104307 |
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