A trust-based collaborative filtering algorithm for E-commerce recommendation system

The rise of e-commerce has not only given consumers more choice but has also caused information overload. In order to quickly find favorite items from vast resources, users are eager for technology by which websites can automatically deliver items in which they may be interested. Thus, recommender systems are created and developed to automate the recommendation process. In the field of collaborative filtering recommendations, the accuracy requirement of the recommendation algorithm always makes it complex and difficult to implement one algorithm. The slope one algorithm is not only easy to implement but also works efficient and effective. However, the prediction accuracy of the slope one algorithm is not very high. Moreover, the slope one algorithm does not perform so well when dealing with personalized recommendation tasks that concern the relationship among users. To solve these problems, we propose a slope one algorithm based on the fusion of trusted data and user similarity, which can be deployed in various recommender systems. This algorithm comprises three procedures. First, we should select trusted data. Second, we should calculate the similarity between users. Third, we need to add this similarity to the weight factor of the improved slope one algorithm, and then, we get the final recommendation equation. We have carried out a number of experiments with the Amazon dataset, and the results prove that our recommender algorithm performs more accurately than the traditional slope one algorithm.

[1]  Srinivasan Venkatesh,et al.  Battling the Internet water army: Detection of hidden paid posters , 2011, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[2]  Chin-Chen Chang,et al.  Public audit for operation behavior logs with error locating in cloud storage , 2019, Soft Comput..

[3]  Jin Wang,et al.  A Variable Threshold-Value Authentication Architecture for Wireless Mesh Networks , 2014 .

[4]  Tao Peng,et al.  Collaborative trajectory privacy preserving scheme in location-based services , 2017, Inf. Sci..

[5]  Zhipeng Zhang,et al.  Applying Covering-Based Rough Set Theory to User-Based Collaborative Filtering to Enhance the Quality of Recommendations , 2015, IUKM.

[6]  Ibtihal Mouhib,et al.  Homomorphic Encryption as a Service for Outsourced Images in Mobile Cloud Computing Environment , 2017, Int. J. Cloud Appl. Comput..

[7]  Siu-Ming Yiu,et al.  HybridORAM: Practical oblivious cloud storage with constant bandwidth , 2018, Inf. Sci..

[8]  Anton van den Hengel,et al.  Image-Based Recommendations on Styles and Substitutes , 2015, SIGIR.

[9]  Daniel Lemire,et al.  Slope One Predictors for Online Rating-Based Collaborative Filtering , 2007, SDM.

[10]  Zilong Zhang,et al.  An Ensemble Random Forest Algorithm for Insurance Big Data Analysis , 2017, CSE/EUC.

[11]  Yufei Chen,et al.  Performance evaluation of implicit smartphones authentication via sensor-behavior analysis , 2018, Inf. Sci..

[12]  Jian Shen,et al.  Finger vein secure biometric template generation based on deep learning , 2018, Soft Comput..

[13]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[14]  Yu Xue,et al.  Efficient business process consolidation: combining topic features with structure matching , 2016, Soft Comput..

[15]  Jian Shen,et al.  Cloud-aided lightweight certificateless authentication protocol with anonymity for wireless body area networks , 2018, J. Netw. Comput. Appl..

[16]  Zhihua Xia,et al.  A Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data , 2016, IEEE Transactions on Parallel and Distributed Systems.

[17]  Luo Si,et al.  An automatic weighting scheme for collaborative filtering , 2004, SIGIR '04.

[18]  SongJie Gong,et al.  Joining User Clustering and Item Based Collaborative Filtering in Personalized Recommendation Services , 2009, 2009 International Conference on Industrial and Information Systems.

[19]  Shihong Huang,et al.  Optimizations for item-based Collaborative Filtering algorithm , 2010, Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010).

[20]  Fucai Zhou,et al.  Dynamic Fully Homomorphic encryption-based Merkle Tree for lightweight streaming authenticated data structures , 2018, J. Netw. Comput. Appl..

[21]  Qu Hongchun,et al.  User-based Collaborative Recommendation Filtering Algorithm Using Extremely Valued Ratings , 2011 .

[22]  Song Guo,et al.  Big Data Meet Green Challenges: Big Data Toward Green Applications , 2016, IEEE Systems Journal.

[23]  Shiyu Song,et al.  A creative personalized recommendation algorithm—User-based Slope One algorithm , 2012, 2012 International Conference on Systems and Informatics (ICSAI2012).

[24]  Sooyoung Kim,et al.  Classification-based collaborative filtering using market basket data , 2005, Expert Syst. Appl..

[25]  Chong-Ben Huang,et al.  Employing rough set theory to alleviate the sparsity issue in recommender system , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[26]  Xuan Li,et al.  Cloud-assisted privacy-preserving profile-matching scheme under multiple keys in mobile social network , 2018, Cluster Computing.

[27]  Jiyun Li,et al.  An improved slope one algorithm for collaborative filtering , 2013, 2013 Ninth International Conference on Natural Computation (ICNC).

[28]  Jie Wu,et al.  Understanding Graph-Based Trust Evaluation in Online Social Networks , 2016, ACM Comput. Surv..

[29]  Nie Gui-hua.  Research of Recommendation Algorithm on Integration of Semantic Similarity and the Item-based CF , 2007 .

[30]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[31]  Rakesh Agrawal,et al.  Privacy-preserving data mining , 2000, SIGMOD 2000.

[32]  Dongqing Xie,et al.  Social influence modeling using information theory in mobile social networks , 2017, Inf. Sci..

[33]  Jie Wu,et al.  Dependable Structural Health Monitoring Using Wireless Sensor Networks , 2015, IEEE Transactions on Dependable and Secure Computing.

[34]  Rajkumar Buyya,et al.  Introduction to Cloud Computing , 2011, CloudCom 2011.

[35]  Ouen Pinngern,et al.  Finding item neighbors in item-based collaborative filtering by adding item content , 2004, ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004..

[36]  SongJie Gong,et al.  A Personalized Recommender Integrating Item-Based and User-Based Collaborative Filtering , 2008, 2008 International Seminar on Business and Information Management.

[37]  Hyung Jun Ahn,et al.  A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem , 2008, Inf. Sci..

[38]  David M. Pennock,et al.  Categories and Subject Descriptors , 2001 .

[39]  Xiaojun Chang,et al.  Semisupervised Feature Analysis by Mining Correlations Among Multiple Tasks , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[40]  Jianfeng Ma,et al.  Identifying opinion leader nodes in online social networks with a new closeness evaluation algorithm , 2018, Soft Comput..

[41]  Jian Shen,et al.  A Novel Routing Protocol Providing Good Transmission Reliability in Underwater Sensor Networks , 2015 .

[42]  Jun Cai,et al.  Enhancing network capacity by weakening community structure in scale-free network , 2017, Future Gener. Comput. Syst..

[43]  Wenbin Chen,et al.  Lattice-based linearly homomorphic signatures in the standard model , 2016, Theor. Comput. Sci..

[44]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[45]  Jian Li,et al.  Application of User-Based Collaborative Filtering Recommendation Technology on Logistics Platform , 2013, 2013 Sixth International Conference on Business Intelligence and Financial Engineering.