Enhancing Rating Prediction Quality Through Improving the Accuracy of Detection of Shifts in Rating Practices

  • Dionisis Margaris
  • Costas VassilakisEmail author
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10940)


The most widely used similarity metrics in collaborative filtering, namely the Pearson Correlation and the Adjusted Cosine Similarity, adjust each individual rating by the mean of the ratings entered by the specific user, when computing similarities, due to the fact that users follow different rating practices, in the sense that some are stricter when rating items, while others are more lenient. However, a user’s rating practices change over time, i.e. a user could start as lenient and subsequently become stricter or vice versa; hence by relying on a single mean value per user, we fail to follow such shifts in users’ rating practices, leading to decreased rating prediction accuracy. In this work, we present a novel algorithm for calculating dynamic user averages, i.e. time-in-point averages that follow shifts in users’ rating practices, and exploit them in both user-user and item-item collaborative filtering implementations. The proposed algorithm has been found to introduce significant gains in rating prediction accuracy, and outperforms other dynamic average computation approaches that are presented in the literature.


Recommender systems Collaborative filtering User-user similarity Item-item similarity Dynamic average Prediction accuracy Ratings’ timestamps 


  1. 1.
    Balabanovic, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)CrossRefGoogle Scholar
  2. 2.
    Dror, G., Koenigstein, N., Koren, Y.: Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy. In: Proceedings of the 5th ACM Conference on Recommender Systems (RecSys 2011), New York, NY, USA, pp. 165–172 (2011)Google Scholar
  3. 3.
    Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007). Scholar
  4. 4.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)CrossRefGoogle Scholar
  5. 5.
    Li, L., Zheng, L., Yang, F., Li, T.: Modeling and broadening temporal user interest in personalized news recommendation. Expert Syst. Appl. 41(7), 3168–3177 (2014)CrossRefGoogle Scholar
  6. 6.
    Burke, R.: Hybrid recommender systems: Survey and experiments. User Model. User-Adap. Interact. 12(4), 331–370 (2002)CrossRefGoogle Scholar
  7. 7.
    McAuley, J.J., Pandey, R., Leskovec, J.: Inferring networks of substitutable and complementary products. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, pp. 785–794 (2015)Google Scholar
  8. 8.
    McAuley, J., Targett, C., Shi, J., van den Hengel, A.: Image-based recommendations on styles and substitutes. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago, Chile, pp. 43–52 (2015)Google Scholar
  9. 9.
    MovieLens datasets. Accessed 22 Sept 2017
  10. 10.
    Harper, F.M., Konstan, J.A.: The MovieLens datasets: history and context. ACM Trans. Interact. Intell. Syst. (TiiS) 5(4), 19 (2015). Article No. 19Google Scholar
  11. 11.
    Zhou, Y., Wilkinson, D., Schreiber, R., Pan, R.: Large-scale parallel collaborative filtering for the netflix prize. In: Fleischer, R., Xu, J. (eds.) AAIM 2008. LNCS, vol. 5034, pp. 337–348. Springer, Heidelberg (2008). Scholar
  12. 12.
    Margaris, D., Vassilakis, C.: Pruning and aging for user histories in collaborative filtering. In: Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence, Athens, Greece, pp. 1–8 (2016)Google Scholar
  13. 13.
    Liu, N.N., He, L., Zhao, M.: Social temporal collaborative ranking for context aware movie recommendation. ACM Trans. Intell. Syst. Technol. (TIST) 4(1) (2013). Article No. 15Google Scholar
  14. 14.
    Bakshy, E., Rosenn, I., Marlow, C., Adamic, L.: The role of social networks in information diffusion. In: Proceedings of the 21st International Conference on World Wide Web, Lyon, France, pp. 519–528 (2012)Google Scholar
  15. 15.
    Koren, Y.: Factor in the neighbors: scalable and accurate collaborative filtering. ACM Trans. Knowl. Discov. Data (TKDD) 4(1) (2010). Article No. 1Google Scholar
  16. 16.
    Vaz, P.C., Ribeiro, R., de Matos, D.M.: Understanding temporal dynamics of ratings in the book recommendation scenario. In: Proceedings of the 2013 International Conference on Information Systems and Design of Communication, ACM ISDOC 2013, New York, NY, USA, pp. 11–15 (2013)Google Scholar
  17. 17.
    Nishida, K., Yamauchi, K.: Detecting concept drift using statistical testing. In: Corruble, V., Takeda, M., Suzuki, E. (eds.) DS 2007. LNCS (LNAI), vol. 4755, pp. 264–269. Springer, Heidelberg (2007). Scholar
  18. 18.
    Liu, H., Hu, Z., Mian, A., Tian, H., Zhu, X.: A new user similarity model to improve the accuracy of collaborative filtering. Knowl.-Based Syst. 56, 156–166 (2014)CrossRefGoogle Scholar
  19. 19.
    Minku, L.L., White, A.P., Yao, X.: The impact of diversity on online ensemble learning in the presence of concept drift. IEEE Trans. Knowl. Data Eng. 22(5), 730–742 (2010)CrossRefGoogle Scholar
  20. 20.
    Elwell, R., Polikar, R.: Incremental learning of concept drift in nonstationary environments. IEEE Trans. Neural Netw. 22(10), 1517–1531 (2011)CrossRefGoogle Scholar
  21. 21.
    Ang, H.H., Gopalkrishnan, V., Zliobaite, I., Pechenizkiy, M., Hoi, S.C.H.: Predictive handling of asynchronous concept drifts in distributed environments. IEEE Trans. Knowl. Data Eng. 25(10), 2343–2355 (2013)CrossRefGoogle Scholar
  22. 22.
    Zliobaite, I., Bakker, J., Pechenizkiy, M.: Beating the baseline prediction in food sales: how intelligent an intelligent predictor is? Expert Syst. Appl. 39(1), 806–815 (2012)CrossRefGoogle Scholar
  23. 23.
    Vaz, P.C., Ribeiro, R., DeMatos, D.M.: Understanding temporal dynamics of ratings in the book recommendation scenario. In: Proceedings of the 2013 International Conference on Information Systems and Design of Communication (ISDOC 2013), Lisbon, Portugal, pp. 11–15 (2013)Google Scholar
  24. 24.
    Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. 1(1) (2013). Article No. 1Google Scholar
  25. 25.
    Margaris, D., Vassilakis, C., Georgiadis, P.: Recommendation information diffusion in social networks considering user influence and semantics. Soc. Netw. Anal. Mining 6(108), 1–22 (2016)Google Scholar
  26. 26.
    Ekstrand, M.D., Riedl, J.T., Konstan, J.A.: Collaborative filtering recommender systems. Found. Trends Hum.-Comput. Interact. 4(2), 81–173 (2011)CrossRefGoogle Scholar
  27. 27.
    He, D., Wu, D.: Toward a robust data fusion for document retrieval. In: Proceedings of the 4th IEEE International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE), Beijing, China, pp. 1–8 (2008)Google Scholar
  28. 28.
    Shardanand, U., Maes, P.: Social information filtering: algorithms for automating “word of mouth”. In: Proceedings of the 1995 SIGCHI Conference on Human Factors in Computing Systems, Denver, Colorado, USA, pp. 210–217 (1995)Google Scholar
  29. 29.
    Margaris, D., Vassilakis, C., Georgiadis, P.: Knowledge-based leisure time recommendations in social networks. In: Alor-Hernández, G., Valencia-García, R. (eds.) Current Trends on Knowledge-Based Systems. Intelligent Systems Reference Library, vol. 120, pp. 23–48. Springer, Cham (2017).
  30. 30.
    Yu, K., Schwaighofer, A., Tresp, V., Xu, X., Kriegel, H.P.: Probabilistic memory-based collaborative filtering. IEEE Trans. Knowl. Data Eng. 16(1), 56–69 (2004)CrossRefGoogle Scholar
  31. 31.
    Dias, R., Fonseca, M.J.: Improving music recommendation in session-based collaborative filtering by using temporal context. In: Proceedings of the 25th IEEE International Conference on Tools with Artificial Intelligence, Herndon, VA, pp. 783–788 (2013)Google Scholar
  32. 32.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. 42(8), 42–49 (2009)CrossRefGoogle Scholar
  33. 33.
    Margaris, D., Vassilakis, C.: Improving collaborative filtering’s rating prediction quality by considering shifts in rating practices. In: Proceedings of the 19th IEEE International Conference on Business Informatics, Thessaloniki, Greece, vol. 01, pp. 158–166 (2017)Google Scholar
  34. 34.
    Bao, J., Zheng, Y., Mokbel, M.: Location-based and preference-aware recommendation using sparse geo-social networking data. In: Proceedings of the 20th International Conferences on Advances in Geographic Information Systems (SIGSPATIAL 2012), Redondo Beach, California, pp. 199–208 (2012)Google Scholar
  35. 35.
    Zheng, Y., Xie, X.: Learning travel recommendations from user-generated GPS traces. ACM Trans. Intell. Syst. Technol. (TIST) 2(1), 29 (2011). Article No. 2Google Scholar
  36. 36.
    Gong, S.: A collaborative filtering recommendation algorithm based on user clustering and item clustering. J. Softw. 5(7), 745–752 (2010)CrossRefGoogle Scholar
  37. 37.
    Das, A., Datar, M., Garg, A., Rajaram, S.: Google news personalization: scalable online collaborative filtering. In: Proceedings of the 16th International Conference on World Wide Web, Banff, Alberta, Canada, pp. 271–280 (2007)Google Scholar
  38. 38.
    Margaris, D., Vassilakis, C.: Enhancing user rating database consistency through pruning. Trans. Large-Scale Data Knowl.-Centered Syst. XXXIV, 33–64 (2017)Google Scholar
  39. 39.
    Ramezani, M., Moradi, P., Akhlaghian, F.: A pattern mining approach to enhance the accuracy of collaborative filtering in sparse data domains. Phys. A: Stat. Mech. Appl. 408, 72–84 (2014)CrossRefGoogle Scholar
  40. 40.
    Najafabadi, M.K., Mahrin, M.N., Chuprat, S., Sarkan, H.M.: Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data. Comput. Hum. Behav. 67, 113–128 (2017)CrossRefGoogle Scholar
  41. 41.
    Li, C., Shan, M., Jheng, S., Chou, K.: Exploiting concept drift to predict popularity of social multimedia in microblogs. Inf. Sci. 339, 310–331 (2016)CrossRefGoogle Scholar
  42. 42.
    Lu, Z., Pan, S.J., Li, Y., Jiang, J., Yang, Q.: Collaborative evolution for user profiling in recommender systems. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI 2016), pp. 3804–3810 (2016)Google Scholar
  43. 43.
    Kangasrääsiö, A., Chen, Y., Głowacka, D., Kaski, S.: Interactive modeling of concept drift and errors in relevance feedback. In: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization (ACM UMAP 2016), New York, NY, USA, pp. 185–193 (2016)Google Scholar
  44. 44.
    Gantner, Z., Rendle, S., Schmidt-Thieme, L.: Factorization models for context-/time-aware movie recommendations. In: Proceedings of the Workshop on Context-Aware Movie Recommendation (ACM CAMRa 2010), New York, NY, USA, pp. 14–19 (2010)Google Scholar
  45. 45.
    Zhang, J.D., Chow, C.Y.: TICRec: a probabilistic framework to utilize temporal influence correlations for time-aware location recommendations. IEEE Trans. Serv. Comput. 9(4), 633–646 (2016)CrossRefGoogle Scholar
  46. 46.
    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 (WWW10), Hong Kong, pp. 285–295 (2001)Google Scholar
  47. 47.
    Winoto, P., Tang, T.Y.: The role of user mood in movie recommendations. Expert Syst. Appl. 37, 6086–6092 (2010)CrossRefGoogle Scholar
  48. 48.
    DELL: PowerEdge R930 Rack Server specs. Accessed 22 Feb 2018
  49. 49.
    Cha, S.-H.: Comprehensive survey on distance/similarity measures between probability density functions. Int. J. Math. Models Methods Appl. Sci. 1(4), 300–307 (2007)MathSciNetGoogle Scholar
  50. 50.
    Son, L.H.: HU-FCF: a hybrid user-based fuzzy collaborative filtering method in recommender systems. Expert Syst. Appl. 41, 6861–6870 (2014)CrossRefGoogle Scholar
  51. 51.
    Guo, G., Zhang, J., Thalmann, D., Yorke-Smith, N.: ETAF: an extended trust antecedents framework for trust prediction. In: Proceedings of the 2014 International Conference on Advances in Social Networks Analysis and Mining ASONAM 2014, Beijing, China, pp. 540–547 (2014)Google Scholar
  52. 52.
    Lo, Y.-Y., Liao, W., Chang, C.-S., Lee, Y.-C.: Temporal matrix factorization for tracking concept drift in individual user preferences. IEEE Trans. Comput. Soc. Syst. 5(1), 156–168 (2018)CrossRefGoogle Scholar
  53. 53.
    Cheng, W., Yin, G., Dong, Y., Dong, H., Zhang, W.: Collaborative filtering recommendation on users’ interest sequences. PLoS One 11(5), e0155739 (2016)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Informatics and TelecommunicationsUniversity of AthensAthensGreece
  2. 2.Department of Informatics and TelecommunicationsUniversity of the PeloponneseTripoliGreece

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