research-article

Cognitive-Based Hybrid Collaborative Filtering with Rating Scaling on Entropy to Defend Shilling Influence

Authors:

Jiaxing Jason Mai,

Carson K. Leung,

Alfredo CuzzocreaAuthors Info & Claims

ICNCC '19: Proceedings of the 2019 8th International Conference on Networks, Communication and Computing

Pages 176 - 185

https://doi.org/10.1145/3375998.3376040

Published: 28 January 2020 Publication History

Abstract

In the current era of big data, huge volumes a wide variety of valuable data are generated and collected at a high velocity. Hence, data science solutions are in demand to data mine these big data for valuable information and useful knowledge embedded in these big data in order to transform this information and knowledge into recommendations and actions. In particular, recommendation systems (RecSys or RS)---which are tools that can provide suggestions to users based on various metrics---have been playing an important role in society since the booming of the Internet. Making more accurate predictions can both potentially increase company revenue and enhance user experience. So, it has been a hot topic. More specifically, collaborative filtering (CF) has been a popular technique applied in RS. The key ideas behind most of the CF algorithms are to filter items based on other users' opinions. Since the recommendation process is based on user interactions, one of the challenges is how to prevent shilling attacks (or shilling attack ratings). In this paper, we propose methods to integrate users' rating entropy into collaborative filtering so as to defend shilling attacks and reduce noisy ratings, and thus achieve higher prediction accuracy. Evaluation results show the effectiveness of our cognitive-based hybrid collaborative filtering methods in rating scaling on entropy for defending shilling influence.

References

[1]

C.C. Aggarwal. 2016. Knowledge-based recommendar systems. In Recommeder Systems, pp. 167--197.

[2]

R. Baeza-Yates, B. Ribeiro-Neto. 1999. Modern information retrieval. Boston, MA, USA: Addison-Wesley Longman.

Digital Library

[3]

L. Bellatreche, A. Cuzzocrea, and S. Benkrid. 2010. F&A: a methodology for effectively and efficiently designing parallel relational data warehouses on heterogenous database clusters. In DaWaK 2010, pp. 89--104.

[4]

J.S. Breese, D. Heckerman, and C. Kadie. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In UAI 1998, pp. 43--52.

Digital Library

[5]

R. Burke. 2002. Hybrid recommender systems: survey and experiments. User Modeling and User-Adapted Interaction, 12(4), 331--370.

Digital Library

[6]

M. Ceci, A. Cuzzocrea, and D. Malerba. 2015. Effectively and efficiently supporting roll-up and drill-down OLAP operations over continuous dimensions via hierarchical clustering. JIIS, 44(3), 309--333.

[7]

D. Cosley, S.K. Lam, I. Albert, J.A. Konstan, and J. Riedl. 2003. Is seeing believing?: how recommender system interfaces affect users' opinions. In CHI 2003, pp. 585--592.

[8]

A. Cuzzocrea and I. Song. 2014. Big graph analytics: the state of the art and future research agenda. In DOLAP 2014, pp. 99--101.

[9]

C. Dellarocas. 2000. Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. In ACM EC 2000, pp. 150--157.

[10]

K.E. Dierckens, A.B. Harrison, C.K. Leung, and A.V. Pind. 2017. A data science and engineering solution for fast k-means clustering of big data. In IEEE TrustComBigDataSE-ICESS 2017, pp. 925--932.

[11]

C. Fan, H. Hao, C.K. Leung, L.Y. Sun, and J. Tran. 2018. Social network mining for recommendation of friends based on music interests. In IEEE/ACM ASONAM 2018, pp. 833--840.

[12]

D. Goldberg, D. Nichols, B.M. Oki, and D. Terry. 1992. Using collaborative filtering to weave an information tapestry. CACM, 35(12), 61--70.

Digital Library

[13]

J.L. Herlocker, J.A. Konstan, L.G. Terveen, and J.T. Riedl. 2004. Evaluating collaborative filtering recommender systems. ACM TOIS, 22(1), 5--53.

Digital Library

[14]

J. Hipp, U. Güntzer, and G. Nakhaeizadeh. 2000. Algorithms for association rule mining---a general survey and comparison. ACM SIGKDD Explorations, 2(1), 58-- 64.

Digital Library

[15]

A. Huang. 2008. Similarity measures for text document clustering. In NZCSRSC 2008, pp. 49--56.

[16]

F. Jiang, C.K. Leung, and A.G.M. Pazdor. 2016. Web page recommendation based on bitwise frequent pattern mining. In IEEE/WIC/ACM WI 2016, pp. 632--635.

[17]

L.V.S. Lakshmanan, C.K. Leung, and R.T. Ng. 2000. The segment support map: scalable mining of frequent itemsets. ACM SIGKDD Explorations, 2(2), 21--27.

Digital Library

[18]

S.K. Lam and J. Riedl. 2004. Shilling recommender systems for fun and profit. In WWW 2004, pp. 393--402.

Digital Library

[19]

C.K. Leung, F. Jiang, E.M. Dela Cruz, and V.S. Elango. 2017. Association rule mining in collaborative filtering. In Collaborative Filtering Using Data Mining and Analysis, pp. 159--179.

[20]

C.K. Leung, F. Jiang, and J. Souza. 2018. Web page recommendation from sparse big web data. In IEEE/WIC/ACM WI 2018, pp. 592--597.

[21]

C.K. Leung, A. Kajal, Y. Won, and J.M.C. Choi. 2019. Big data analytics for personalized recommendation systems. In IEEE DASC-PICom-CBDCom-CyberSciTech 2019, pp. 1060--1065.

[22]

C.K. Leung, W. Lee, and J.J. Song. 2019. Information technology-based patent retrieval model. In Springer Handbook of Science and Technology Indicators, pp. 859--874.

[23]

C.K. Leung and Y. Zhang. 2019. An HSV-based visual analytic system for data science on music and beyond. International Journal of Art, Culture and Design Technologies (IJACDT), 8(1), 68--83.

[24]

W. Lin, S.A. Alvarez, and C. Ruiz. 2002. Efficient adaptive-support association rule mining for recommender systems. Data Mining and Knowledge Discovery, 6(1), 83--105.

Digital Library

[25]

M. O'Mahony, N. Hurley, N. Kushmerick, and G. Silvestre. 2004. Collaborative recommendation: A robustness analysis. ACM TOIT, 4(4), 344--377.

Digital Library

[26]

M.J. Pazzani and D. Billsus. 2007. Content-based recommendation systems. In The Adaptive Web, pp. 325--341.

[27]

H. Phan. 2018. NOV-CFI: a novel algorithm for closed frequent itemsets mining in transactional databases. In ICNCC 2018, pp. 58--63.

Digital Library

[28]

N. Polatidis and C.K. Georgiadis. 2016. A multi-level collaborative filtering method that improves recommendations. ESWA, vol. 48, 100--110.

Digital Library

[29]

D. M. Powers. 2011. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. Journal of Machine Learning Technologies, 2(1), 37--63.

[30]

P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. 1994. GroupLens: an open architecture for collaborative filtering of netnews. In ACM CSCW 1994, pp. 175--186.

[31]

F. Ricci, L. Rokach, and B. Shapira. 2015. Recommender systems: introduction and challenges. In Recommender systems handbook, pp. 1--34.

[32]

B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In WWW 2001, pp. 285--295.

Digital Library

[33]

J. B. Schafer, D. Frankowski, J. Herlocker, and S. Sen. 2007. Collaborative filtering recommender systems. In The Adaptive Web, pp. 291--324.

[34]

A.I. Schein, A. Popescul, L.H. Ungar, and D.M. Pennock. 2002. Methods and metrics for cold-start recommendations. In ACM SIGIR 2002, pp. 253--260.

Digital Library

[35]

X. Su and T.M. Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Advances in artificial intelligence, vol. 2009, 421425:1--421425:19.

[36]

R.Y. Toledo, Y.C. Mota, and L. Martínez. 2015. Correcting noisy ratings in collaborative recommender systems. KBS, vol. 76, pp. 96--108.

Digital Library

[37]

K. Zahra, F. Azam, W.H. Butt, and F. Ilyas. 2018. A framework for user characterization based on tweets using machine learning algorithms. In ICNCC 2018, pp. 11--16.

Cited By

Leung CJiang FZhang Y(2020)Explainable Machine Learning and Mining of Influential Patterns from Sparse Web2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WIIAT50758.2020.00128(829-836)Online publication date: Dec-2020
https://doi.org/10.1109/WIIAT50758.2020.00128
Sundar ALi FZou XGao TRussomanno E(2020)Understanding Shilling Attacks and Their Detection Traits: A Comprehensive SurveyIEEE Access10.1109/ACCESS.2020.30229628(171703-171715)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3022962
Leung C(2020)Big Data Computing and Mining in a Smart WorldBig Data Analyses, Services, and Smart Data10.1007/978-981-15-8731-3_2(15-27)Online publication date: 11-Sep-2020
https://doi.org/10.1007/978-981-15-8731-3_2

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cover image ACM Other conferences

ICNCC '19: Proceedings of the 2019 8th International Conference on Networks, Communication and Computing

December 2019

263 pages

ISBN:9781450377027

DOI:10.1145/3375998

Copyright © 2019 ACM.

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Published: 28 January 2020

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ICNCC 2019

ICNCC 2019: 2019 The 8th International Conference on Networks, Communication and Computing

December 13 - 15, 2019

Luoyang, China

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Cited By

Leung CJiang FZhang Y(2020)Explainable Machine Learning and Mining of Influential Patterns from Sparse Web2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WIIAT50758.2020.00128(829-836)Online publication date: Dec-2020
https://doi.org/10.1109/WIIAT50758.2020.00128
Sundar ALi FZou XGao TRussomanno E(2020)Understanding Shilling Attacks and Their Detection Traits: A Comprehensive SurveyIEEE Access10.1109/ACCESS.2020.30229628(171703-171715)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3022962
Leung C(2020)Big Data Computing and Mining in a Smart WorldBig Data Analyses, Services, and Smart Data10.1007/978-981-15-8731-3_2(15-27)Online publication date: 11-Sep-2020
https://doi.org/10.1007/978-981-15-8731-3_2

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