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Robust Recommendation Method Based on Shilling Attack Detection and matrix factorization model

Robust Recommendation Method Based on Shilling Attack Detect...

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2017 2nd International Conference on Communications, Information Management and Network Security (CIMNS2017)

作者： Yu-qi HU Kai LIU Fu-zhi ZHANG School of Information Science and Engineering Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province

The existing robust collaborative recommendation algorithms have low robustness against PIA and Ao P attacks. Aiming at the problem, we propose a robust recommendation method based on shilling attack detection and matrix factorization model. Firstly, the type of shilling attack is identified based on statistical characteristics of attack profiles. Secondly, we devise corresponding unsupervised detection algorithms for standard attack, Ao P and PIA, and the suspicious users and items are flagged. Finally, we devise a robust recommendation algorithm by combining the proposed shilling attack detection algorithm with matrix factorization model, and conduct experiments on the Movie Lens dataset to demonstrate its effectiveness. Experimental results show that the proposed method exhibits good recommendation precision and excellent robustness for shilling attacks of multiple types.

关键词： Collaborative recommendation Shilling attack Attack type identification Attack detection matrix factorization model

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Probabilistic matrix factorization Based on Similarity Propagation and Trust Propagation for Recommendation 1

Probabilistic Matrix Factorization Based on Similarity Propa...

引用

IEEE Conference Collaboration Internet Computing

作者： Zhao, Haiyan Wang, Shengsheng Chen, Qingkui Cao, Jian Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9781509000890

Traditional collaborative filtering approaches are often confronted with two major problems: data sparsity and cold-start. Fortunately, along with the rise of social media, social network is producing a large and rich set of social data (such as labels, trust, etc.), which provides a new way to solve the problems of collaborative filtering, namely, we can make use of social data to enhance the recommendation accuracy. However, traditional recommendation algorithms may only consider either the influence of similarity relationships or trust relationships to the user model, but fail to take full advantage of the implications of social data. In this paper, we propose a novel recommendation algorithm called STPMF based on neighborhood model and matrix factorization model, where complementary roles of similarity relationships and trust relationships to the user model by means of a weight w are considered simultaneously. Furthermore, we propagate similarity relationships and trust relationships one step or two steps to alleviate the data sparsity and cold-start problems. We have conducted experiments on two real world data sets from *** and Delicious. Compared with existing recommendation algorithms, our method can effectively alleviate the problems of collaborative filtering, and enhance the recommendation accuracy.

关键词： collaborative filtering social network neighborhood model matrix factorization model similarity propagation trust propagation

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matrix Decomposition model of E-commerce Based On Collaborative Filtering Algorithm

Matrix Decomposition Model of E-commerce Based On Collaborat...

引用

International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI)

作者： Li, Lin Dalian Univ Sci & Technol Dalian Peoples R China

ISBN: (数字)9781510651890

ISBN: (纸本)9781510651890;9781510651883

Currently, there is a distinct contradiction between the massive information and the lack of personalized information. In this case, the recommendation system has been widely used as an important technology to discover the potential interests of users and recommend the items of interest to the target users. Considering that the probability matrix decomposition model can show its prediction mechanism more clearly, in this paper, the probabilistic matrix factorization model is used in the matrix factorization method. Based on the probabilistic matrix factorization, a recommendation method framework considering both extreme rating behavior similarity and rating matrix information fusion is proposed. The framework integrates the local neighbor relationship of users into the global rating optimization process of matrix factorization, and thus improves the prediction accuracy and robustness of sparse data. Simulation results show that the proposed method reduces the MAE by 0.68%, 1.12%, 2.85% and 1.19% compared with the suboptimal method. It is proved that the proposed method can effectively implement long tail project recommendation and ensure high recommendation accuracy.

关键词： E-commerce Search engine matrix factorization model Collaborative filtering algorithm FPMF algorithm

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Utilizing Favorites Lists for Better Recommendations 4

Utilizing Favorites Lists for Better Recommendations

引用

IEEE Fourth International Conference on Big Data and Cloud Computing (BdCloud)

作者： Abualsaud, Mustafa Thomo, Alex Univ Victoria Victoria BC Canada

ISBN: (纸本)9781479967193

We present two new models that take into account the information available in user-created "favorites" lists for enhancing the quality of item recommendation. The first model uses the popularity and ratings of items in the lists to predict ratings for new items to users that have rated some items on the lists. The second model is a matrix factorization model that incorporates lists as implicit feedback in ratings prediction. We compare our two approaches against another work for utilizing favorites lists, as well as the popular Singular Value Decomposition (SVD) on two large Amazon datasets and show that utilizing favorites lists gives significant improvements, especially in cold-start cases.

关键词： recommender systems singular value decomposition Amazon datasets SVD implicit feedback item recommendation quality matrix factorization model rating prediction user-created favorite lists Computational modeling Graphical models Prediction algorithms Predictive models Recommender systems Vectors YouTube Singular value decomposition Recommender systems LIST Prediction algorithms Graphical models Swine vesicular disease YouTube Favorites Computational modeling matrix decomposition Prediction models Rating

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