Frontiers of Computer Science

, Volume 13, Issue 2, pp 231–246 | Cite as

From similarity perspective: a robust collaborative filtering approach for service recommendations

  • Min GaoEmail author
  • Bin Ling
  • Linda Yang
  • Junhao Wen
  • Qingyu Xiong
  • Shun Li
Research Article


Collaborative filtering (CF) is a technique commonly used for personalized recommendation and Web service quality-of-service (QoS) prediction. However, CF is vulnerable to shilling attackers who inject fake user profiles into the system. In this paper, we first present the shilling attack problem on CF-based QoS recommender systems for Web services. Then, a robust CF recommendation approach is proposed from a user similarity perspective to enhance the resistance of the recommender systems to the shilling attack. In the approach, the generally used similarity measures are analyzed, and the DegSim (the degree of similarities with top k neighbors) with those measures is selected for grouping and weighting the users. Then, the weights are used to calculate the service similarities/differences and predictions.We analyzed and evaluated our algorithms using WS-DREAM and Movielens datasets. The experimental results demonstrate that shilling attacks influence the prediction of QoS values, and our proposed features and algorithms achieve a higher degree of robustness against shilling attacks than the typical CF algorithms.


collaborative filtering service recommendation system robustness shilling attack 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



This research is supported by the Basic and Advanced Research Projects in Chongqing (cstc2015jcyjA40049), the National Natural Science Foundation of China (Grant No. 71102065), the Fundamental Research Funds for the Central Universities (106112014 CDJZR 095502), and the China Scholarship Council.

Supplementary material

11704_2017_6566_MOESM1_ESM.ppt (308 kb)
Supplementary material, approximately 307 KB.


  1. 1.
    Zheng Z B, Ma H, Lyu MR, King I. Collaborative Web service qos prediction via neighborhood integrated matrix factorization. IEEE Transactions on Services Computing, 2013, 6(3): 289–299CrossRefGoogle Scholar
  2. 2.
    Hernando A, Bobadilla J, Ortega F. A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model. Knowledge-Based Systems, 2016, 97: 188–202CrossRefGoogle Scholar
  3. 3.
    Xu J L, Zheng Z B, Lyu M R. Web service personalized quality of service prediction via reputation-based matrix factorization. IEEE Transactions on Reliability, 2016, 65(1): 28–37CrossRefGoogle Scholar
  4. 4.
    Jiang S H, Qian X M, Shen J L, Fu Y, Mei T. Author topic modelbased collaborative filtering for personalized POI recommendations. IEEE Transactions on Multimedia, 2015, 17(6): 907–918Google Scholar
  5. 5.
    Mobasher B, Burke R, Sandvig J J. Model-based collaborative filtering as a defense against profile injection attacks. In: Proceedings of AAAI Conference on Artificial Intelligence. 2006, 1388–1393Google Scholar
  6. 6.
    Hurley N, Cheng Z P, Zhang M. Statistical attack detection. In: Proceedings of the 3rd ACMConference on Recommender Systems. 2009, 149–156Google Scholar
  7. 7.
    Zhang S, Ouyang Y, Ford J, Makedon F. Analysis of a low-dimensional linear model under recommendation attacks. In: Proceedings of the 29th Annual ACM SIGIR Conference on Research and Development in Information Retrieval. 2006, 517–524CrossRefGoogle Scholar
  8. 8.
    Sarwar B, Karypis G, Konstan J, Riedl J. Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web. 2001, 285–295Google Scholar
  9. 9.
    Mobasher B, Burke R, Bhaumik R, Williams C. Toward trustworthy recommender systems: an analysis of attack models and algorithm robustness. ACM Transactions on Internet Technology (TOIT), 2007, 7(4): 2301–2338CrossRefGoogle Scholar
  10. 10.
    Zhou Q Q. Supervised approach for detecting average over popular items attack in collaborative recommender systems. IET Information Security, 2016, 10(3): 134–141MathSciNetCrossRefGoogle Scholar
  11. 11.
    Chirita P A, Nejdl W, Zamfir C. Preventing shilling attacks in online recommender systems. In: Proceedings of the 7th Workshop on Web Information and Data Management. 2005, 67–74Google Scholar
  12. 12.
    Yang Z H, Cai Z, Guan X H. Estimating user behavior toward detecting anomalous ratings in rating systems. Knowledge-Based Systems, 2016, 111: 114–158CrossRefGoogle Scholar
  13. 13.
    Zhang Z, Kulkarni S R. Detection of shilling attacks in recommender systems via spectral clustering. In: Proceedings of the 17th IEEE Conference on Information Fusion. 2014, 1–8Google Scholar
  14. 14.
    Cao J, Wu Z, Mao B, Zhang Y C. Shilling attack detection stilizing semi-supervised learning method for collaborative recommender system. World Wide Web, 2013, 16(5): 729–748CrossRefGoogle Scholar
  15. 15.
    Wu Z, Wang Y Q, Wang Y Q, Wu J J, Cao J, Zhang L. Spammers detection from product reviews: a hybrid model. In: Proceedings of IEEE Conference on Data Mining. 2015, 1039–1044Google Scholar
  16. 16.
    O’Donovan J, Smyth B. Trust in recommender Systems. In: Proceedings of the 10th Conference on Intelligent User Interfaces. 2005, 39(4): 167–174Google Scholar
  17. 17.
    Moradi P, Ahmadian S. A reliability-based recommendation method to improve trust-aware recommender systems. Expert Systems with Applications, 2015, 42(21): 7386–7398CrossRefGoogle Scholar
  18. 18.
    Xia H, Fang B, Gao M, Ma H, Tang Y Y, Wen J. A novel item anomaly detection approach against Shilling attacks in collaborative recommendation systems using the dynamic time interval segmentation technique. Information Sciences, 2015, 306: 150–165CrossRefGoogle Scholar
  19. 19.
    Mobasher B, Burke R, Williams C, Bhaumik R. Analysis and detection of segment-focused attacks against collaborative recommendation. In: Proceedings of Advances in Web Mining and Web Usage Analysis. 2006, 96–118CrossRefGoogle Scholar
  20. 20.
    Mobasher B, Burke R, Bhaumik R, Williams C. Effective attack models for shilling item-based collaborative filtering systems. In: Proceedings of KDD Workshop on Web Mining and Web Usage Analysis. 2005, 21–28Google Scholar
  21. 21.
    Chen X, Zheng Z B, Yu Q, LyuM. R.Web service recommendation via exploiting location and qos information. IEEE Transactions on Parallel and Distributed Systems, 2014, 25(7): 1913–1924CrossRefGoogle Scholar
  22. 22.
    Mehta B, Hofmann T, Fankhauser P. Lies and propaganda: detecting spam users in collaborative filtering. In: Proceedings of the 12th ACM Conference on Intelligent User Interfaces. 2007, 14–21CrossRefGoogle Scholar
  23. 23.
    Wang G J, Musau F, Guo S, Abdullahi M B. Neighbor similarity trust against sybil attack in P2P e-commerce. IEEE Transactions on Parallel and Distributed Systems, 2015, 26(3): 824–833CrossRefGoogle Scholar
  24. 24.
    O’Mahony M, Hurley N, Kushmerick N. Collaborative recommendation: a robustness analysis. ACM Transactions on Internet Technology, 2004, 4(4): 344–377CrossRefGoogle Scholar
  25. 25.
    Shang M S, Lu L Y, Zeng W, Zhang Y C, Zhou T. Relevance is more significant than correlation: information filtering on sparse data. Europhysics Letters, 2009, 88(6): 68008CrossRefGoogle Scholar
  26. 26.
    Ziegler C N, Golbeck J, Investigating interactions of trust and interest similarity. Decision Support Systems, 2007, 43(2): 460–475CrossRefGoogle Scholar
  27. 27.
    Lee D H, Brusilovsky P. Social networks and interest similarity: the case of CiteULike. In: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia. 2010, 151–156CrossRefGoogle Scholar
  28. 28.
    Oh H K, Kim S W, Robust features for trustable aggregation of online ratings. In: Proceedings of the 10th ACM Conference on Ubiquitous Information Management and Communication. 2016, 13–19Google Scholar
  29. 29.
    Kriegel H P, Kroger P, Sander J, Zimek A. Density-based clustering. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2011, 1(3): 231–240Google Scholar
  30. 30.
    Ester M, Kriegel H P, Sander J, Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of KDD Workshop on Web Mining and Web Usage Analysis. 1996, 226–231Google Scholar
  31. 31.
    Lemire D, Maclachlan A. Slope one predictors for online rating-based collaborative filtering. In: Proceedings of SIAM Data Mining. 2005, 1–5Google Scholar
  32. 32.
    O’Mahony M P, Hurley N J, Silvestre G C. Promoting recommendations: an attack on collaborative filtering. Database and Expert Systems Applications. Springer Berlin Heidelberg, 2002, 2453: 494–503zbMATHGoogle Scholar
  33. 33.
    Cao J, Wu Z A, Wang Y Q, Zhuang Y. Hybrid collaborative filtering algorithm for bidirectional Web service recommendation. Knowledge and Information Systems, 2013, 36(3): 607–627CrossRefGoogle Scholar
  34. 34.
    Yao L, Sheng Q Z, Segev A, Yu J. Recommending Web services via combining collaborative filtering with content-based features. In: Proceedings of the 20th IEEE Conference on Web Services. 2013, 42–49Google Scholar
  35. 35.
    Zheng Z B, Ma H, Lyu M R, King l. QoS-aware Web service recommendation by collaborative filtering. IEEE Transaction on Services Computing, 2011, 4(2): 140–152CrossRefGoogle Scholar
  36. 36.
    Jung J J, Attribute selection-based recommendation framework for short-head user group: an empirical study by movieLens and IMDB. Expert Systems with Applications, 2012, 39(4): 4049–4054CrossRefGoogle Scholar
  37. 37.
    Rong W G, Liu K C, Liang L. Personalized Web service ranking via user group combining association rule. In: Proceedings of IEEE Conference on Services Computing. 2009, 445–452Google Scholar
  38. 38.
    Rong W G, Peng B L, Ouyang Y, Liu K C, Xiong Z. Collaborative personal profiling for Web service ranking and recommendation. Information Systems Frontiers, 2015, 17(6): 1265–1282CrossRefGoogle Scholar
  39. 39.
    Chen X, Liu X D, Huang Z C, Sun H L. Region kNN: a scalable hybrid collaborative filtering algorithm for personalized Web service recommendation. In: Proceedings of IEEE Conference on Web Services. 2010, 9–16Google Scholar
  40. 40.
    Bryan K, O’ Mahony M, Cunningham P. Unsupervised retrieval of attack profiles in collaborative recommender systems. In: Proceedings of ACM Conference on Recommender Systems. 2008, 155–162Google Scholar
  41. 41.
    Massa P, Avesani P. Trust-aware recommender systems. In: Proceedings of ACM Conference on Recommender Systems. 2007, 17–24Google Scholar
  42. 42.
    Zhang F G. Research on trust based collaborative filtering algorithm for user’s multiple interests. Journal of Chinese Computer System, 2008, 29(8): 1415–1419Google Scholar
  43. 43.
    Mehta B, Nejdl W. Attack resistant collaborative filtering. In: Proceedings of the 31st ACM Conference on Research and Development in Information Retrieval. 2008, 75–82Google Scholar
  44. 44.
    Hurley N J, Robustness of recommender systems. In: Proceedings of the 5th ACM Conference on Recommender System. 2011, 9–10CrossRefGoogle Scholar
  45. 45.
    Douceur J R. The sybil attack. In: Proceedings of International Workshop on Peer-to-peer Systems. 2002, 251–260CrossRefGoogle Scholar
  46. 46.
    Karlof C, DavidW. Secure routing in wireless sensor networks: attacks and countermeasures. Ad Hoc Networks, 2003, 1(2): 293–315CrossRefGoogle Scholar
  47. 47.
    Newsome J, Shi E, Song D, Perrig A. The sybil attack in sensor networks: analysis & defenses. In: Proceedings of the 3rd ACM International Symposium on Information Processing in Sensor Networks. 2004, 259–268Google Scholar
  48. 48.
    Yu H F, Shi C W, Kaminsky M, Gibbons P B, Xiao F. Dsybil: optimal sybil-resistance for recommendation systems. In: Proceedings of the 30th IEEE Symposium on Security and Privacy. 2009, 283–298Google Scholar
  49. 49.
    Noh G, Kang Y M, Oh H, Kim C K. Robust sybil attack defense with information level in online recommender systems. Expert Systems with Applications, 2014, 41(4): 1781–1791CrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Min Gao
    • 1
    • 2
    Email author
  • Bin Ling
    • 3
  • Linda Yang
    • 3
  • Junhao Wen
    • 1
    • 2
  • Qingyu Xiong
    • 1
    • 2
  • Shun Li
    • 1
    • 2
  1. 1.Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University)Ministry of EducationChongqingChina
  2. 2.School of Software EngineeringChongqing UniversityChongqingChina
  3. 3.School of EngineeringUniversity of PortsmouthPortsmouthUK

Personalised recommendations