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Current Trends in Collaborative Filtering Recommendation Systems

  • Sana Abida Amin
  • James Philips
  • Nasseh TabriziEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11517)

Abstract

Many different approaches for designing recommendation systems exist, including collaborative filtering, content-based, and hybrid approaches. Following an overview of different collaborative filtering recommendation system design methodologies, this paper reviews 71 journals articles and conference papers to provide a detailed literature review of model-based collaborative filtering. The articles selected for this review were published within the last decade between 2008–2018. They are classified by database, application field, methodology, and publication year. Papers using Clustering, Bayesian, Association Rule, Neural Networks, Regression, and Ensemble methodologies are surveyed. Application areas include books, music, movies, social networks, and business. This survey also analyzes the type of the data that was used for application field. This literature review identifies trends for model-based collaborative filtering and through empirical results gives insight into future research trajectories in this field.

Keywords

Collaborative filtering Recommendation system Methodologies Applications 

References

  1. 1.
    Shah, K., Salunke, A., Dongare, S., Antala, K.: Recommender systems: an overview of different approaches to recommendations. In: International Conference on Innovations in Information, Embedded and Communication Systems, pp. 1–4. IEEE (2017)Google Scholar
  2. 2.
    Sharma, M., Mann, S.: A survey of recommender systems: approaches and limitations. Int. J. Innov. Eng. Technol. 2(2), 8–14 (2013)Google Scholar
  3. 3.
    Aditya, P., Budi, I., Munajat, Q.: A comparative analysis of memory-based and model-based collaborative filtering on the implementation of recommender system for e-commerce in Indonesia: a case study PT X. In: International Conference on Advanced Computer Science and Information Systems, pp. 303–308. IEEE (2016)Google Scholar
  4. 4.
    Khatwani, S., Chandak, M.: Building personalized and non-personalized recommendation systems. In: International Conference on Automatic Control and Dynamic Optimization Techniques, pp. 623–628. IEEE (2016)Google Scholar
  5. 5.
    Isinkaye, F., Folajimi, Y., Ojokoh, B.: Recommendation systems: principles, methods, and evaluation. Egyptian Inform. J. 16(3), 261–273 (2015)Google Scholar
  6. 6.
    Pareek, J., Jhaveri, M., Kapasi, A., Trivedi, M.: SNetRS: social networking in recommendation system. In: Meghanathan, N., Nagamalai, D. (eds.) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol. 177. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-31552-7_21Google Scholar
  7. 7.
    Liphoto, M., Du, C., Ngwira, S.: A survey on recommender systems. In: International Conference on Advances in Computing and Communication Engineering, pp. 276–280. IEEE (2016)Google Scholar
  8. 8.
    Kim, K., Ahn, H.: A recommender system using GA k-means clustering in an online shopping market. Expert Syst. Appl. 34(2), 1200–1209 (2008)Google Scholar
  9. 9.
    Sun, X., Kong, F., Chen, H.: Using quantitative association rules in collaborative filtering. In: Fan, W., Wu, Z., Yang, J. (eds.) WAIM 2005. LNCS, vol. 3739, pp. 822–827. Springer, Heidelberg (2005).  https://doi.org/10.1007/11563952_87Google Scholar
  10. 10.
    Kim, E., Kim, M., Ryu, J.: Collaborative filtering based on neural networks using similarity. In: Wang, J., Liao, X.F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3498, pp. 355–360. Springer, Heidelberg (2005).  https://doi.org/10.1007/11427469_57Google Scholar
  11. 11.
    Ge, X., Liu, J., Qi, Q., Chen, Z.: A new prediction approach based on linear regression for collaborative filtering. In: Eighth International Conference on Fuzzy Systems and Knowledge Discovery, pp. 2586–2590. IEEE (2011)Google Scholar
  12. 12.
    Bar, A., Rokach, L., Shani, G., Shapira, B., Schclar, A.: Boosting simple collaborative filtering models using ensemble methods. Arkiv, 21 pp. (2012)Google Scholar
  13. 13.
    Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl.-Based Syst. 46, 109–132 (2013)Google Scholar
  14. 14.
    Ahmed, M., Imtiaz, M., Khan, R.: Movie recommendation system using clustering and pattern recognition network. In: 8th Annual Computing and Communication Workshop and Conference, pp. 143–147. IEEE (2018)Google Scholar
  15. 15.
    Ayaki, T., Yanagimoto, H., Yoshioka, M.: Recommendation from access logs with ensemble learning. Artif. Life Robot. 22(2), 163–167 (2017)Google Scholar
  16. 16.
    Aytekin, T., Karakaya, M.: Clustering-based diversity improvement in top-n recommendation. J. Intell. Inf. Syst. 42(1), 1–18 (2014)Google Scholar
  17. 17.
    Bai, T., Wen, J., Zhang, J., Zhao, W.: A neural collaborative filtering model with interaction-based neighborhood. In: Proceedings of the 2017 ACM Conference on Information and Knowledge Management, pp. 1979–1982. ACM (2017)Google Scholar
  18. 18.
    Barbieri, N., Costa, G., Manco, G., Ortale, R.: Modeling item selection and relevance for accurate recommendations: a Bayesian approach. In: Proceedings of the 5th ACM Conference on Recommender Systems, pp. 21–28. ACM (2011)Google Scholar
  19. 19.
    Beutel, A., Murray, K., Faloutsos, C., Smola, A.: Cobafi: collaborative Bayesian filtering. In: Proceedings of the 23rd International Conference on the World Wide Web, pp. 97–108. ACM (2014)Google Scholar
  20. 20.
    Bi, X., Jin, W.: An improved collaborative filtering similarity model based on neural networks. In: International Conference on Intelligent Transportation, Big Data, and Smart Cities, pp. 85–89. IEEE (2015)Google Scholar
  21. 21.
    Cakir, O., Aras, M.: A recommendation engine by using association rules. Procedia Soc. Behav. Sci. 62, 452–456 (2012)Google Scholar
  22. 22.
    Chatzis, S.: Nonparametric Bayesian multitask collaborative filtering. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, pp. 2149–2158. ACM (2013)Google Scholar
  23. 23.
    Chen, T., Sun, Y., Shi, Y., Hong, L.: On sampling strategies for neural network-based collaborative filtering. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 767–776. ACM (2017)Google Scholar
  24. 24.
    Tsai, C., Hung, C.: Cluster ensembles in collaborative filtering recommendation. Appl. Soft Comput. 12(4), 1417–1425 (2012)Google Scholar
  25. 25.
    Cho, Y., Moon, S., Jeong, S.: Learning listener’s preference for music recommender system. In: Proceedings of the 2015 International Conference on Big Data Applications and Services, pp. 229–232. ACM (2015)Google Scholar
  26. 26.
    Ebesu, T., Fang, Y.: Neural semantic personalized ranking for item cold-start recommendation. Inf. Retrieval J. 20(2), 109–131 (2017)Google Scholar
  27. 27.
    Harper, F., Konstan, J.: The movielens datasets: history and context. ACM Trans. Interactive Intell. Syst. 5(4), 19 (2016)Google Scholar
  28. 28.
    Gao, S., Guo, G., Lin, Y., Zhang, X., Liu, Y., Wang, Z.: Pairwise preference over mixed-type item-sets based Bayesian personalized ranking for collaborative filtering. In: 15th International Conference on Pervasive Intelligence and Computing, pp. 30–37. IEEE (2017)Google Scholar
  29. 29.
    Garcia, E., Romero, C., Ventura, S., De Castro, C.: A collaborative educational association rule mining tool. Internet High. Educ. 14(2), 99–132 (2009)Google Scholar
  30. 30.
    Garcia, E., Romero, C., Ventura, S., De Castro, C.: An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering. User Modeling User-Adapted Interact. 19, 99–132 (2009)Google Scholar
  31. 31.
    Gong, S., Ye, H., Tan, H.: Combining memory-based and model-based collaborative filtering in recommender system. In: Pacific-Asia Conference on Circuits, Communications, and Systems. IEEE (2009)Google Scholar
  32. 32.
    He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182. ACM (2017)Google Scholar
  33. 33.
    Hwang, C., Kao, Y., Yu, P.: Integrating multiple linear regression and multicriteria collaborative filtering for better recommendation. In: 2010 International Conference on Computational Aspects of Social Networks, pp. 229–232. IEEE (2010)Google Scholar
  34. 34.
    Javari, A., Jalili, M.: Cluster-based collaborative filtering for sign prediction in social networks with positive and negative links. ACM Trans. Intell. Syst. Technol. 5(2), 24 (2014)Google Scholar
  35. 35.
    Jiang, M., Yang, Z., Zhao, C.: What to play next? A RNN-based music recommendation system. In: 51st Asilmar Conference on Signals, Systems, and Computers, pp. 356–358. IEEE (2017)Google Scholar
  36. 36.
    Jooa, J., Bangb, S., Parka, G.: Implementation of recommendation system using association rules and collaborative filtering. Procedia Comput. Sci. 91, 944–952 (2016)Google Scholar
  37. 37.
    Paradarami, T., Bastian, N., Wightman, J.: A hybrid recommender system using artificial neural networks. Expert Syst. Appl. 83, 300–313 (2017)Google Scholar
  38. 38.
    Kant, S., Mahara, T.: Nearest bi-clusters collaborative filtering framework with fusion. J. Comput. Sci. 25, 204–212 (2017)Google Scholar
  39. 39.
    Kazienko, P., Pilarczyk, M.: Hyperlink recommendation based on positive and negative association rules. New Gen. Comput. 26(3), 227–244 (2008)Google Scholar
  40. 40.
    Kiran, R., Kitsuregawa, M.: An improved neighborhood-restricted association rule-based recommender system. In: Proceedings of the 24th Australasian Database Conference, pp. 43–50 (2013)Google Scholar
  41. 41.
    Koohi, H., Kiani, K.: User based collaborative filtering using fuzzy c-means. Measurement 91, 134–139 (2016)Google Scholar
  42. 42.
    Li, J., et al.: Category preferred canopy-k-means based collaborative filtering algorithm. Future Gen. Comput. Syst. 93, 1046–1054 (2018)Google Scholar
  43. 43.
    Li, W., Li., X., Yao, M., Jiang, J., Jin, Q.: Personalized fitting recommendation based on support vector regression. Hum.-Centric Comput. Inf. Sci. 5(1), 21 (2015)Google Scholar
  44. 44.
    Lin, K., Wang, J., Zhang, Z., Chen, Y., Xu, Z.: Adaptive location recommendation algorithm based on location-based social networks. In: 10th International Conference on Computer Science and Education, pp. 137–142. IEEE (2015)Google Scholar
  45. 45.
    Liu, C., Jin, T., Hoi, S., Zhao, P., Sun, J.: Collaborative topic regression for online recommender systems: an online and Bayesian approach. Mach. Learn. 106(5), 651–670 (2017)MathSciNetzbMATHGoogle Scholar
  46. 46.
    Liu., Y.: Data mining of university library management based on improved collaborative filtering association rules algorithm. Wirel. Pers. Commun. 102, 3781–3790 (2018)Google Scholar
  47. 47.
    Liu, Y., Wang, S., Khan, M., He, J.: A novel deep hybrid recommender system based on auto-encoder with neural collaborative filtering. Big Data Mining Anal. 1(3), 211–221 (2018)Google Scholar
  48. 48.
    Logesh, R., Subramaniyaswamy, V., Vijayakumar, V., Gao, X., Indragandhi, V.: A hybrid quantum-induced warm intelligence clustering for the urban trip recommendation in smart city. Future Gen. Comput. Syst. 83, 653–673 (2017)Google Scholar
  49. 49.
    Lopes, R., Assunção, R., Santos, R.: Efficient Bayesian methods for graph-based recommendation. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 333–340. ACM (2016)Google Scholar
  50. 50.
    Luo, C., Cai, X.: Bayesian wishart matrix factorization. Data Mining Knowl. Discov. 30(5), 1166–1191 (2016)MathSciNetzbMATHGoogle Scholar
  51. 51.
    Ma, Z., Yang, Y., Wang, F., Li, C., Li, L.: The SOM based improved k-means clustering collaborative filtering algorithm in TV recommendation system. In: Second International Conference on Advanced Cloud and Big Data, pp. 288–295 (2014)Google Scholar
  52. 52.
    Margaris, D., Georgiadis, P., Vassilakis, C.: A collaborative filtering algorithm with clustering for personalized web service selection in business processes. In: 9th International Conference on Research Challenges in Information Science, pp. 169–180 (2015)Google Scholar
  53. 53.
    Maurya, A., Telang, R.: Bayesian multi-view models for member-job matching and personalized skill recommendations. In: IEEE International Conference on Big Data, pp. 1193–1202. IEEE (2017)Google Scholar
  54. 54.
    Mittal, N., Nayak, R., Govil, M., Jain, K.: Recommender system framework using clustering and collaborative filtering. In: 3rd International Conference on Emerging Trends in Engineering and Technology, pp. 555–558. IEEE (2010)Google Scholar
  55. 55.
    Nagarnaik, P., Thomas, A.: Survey on recommendation system methods. In: 2nd International Conference on Electronics and Communication Systems, pp. 1496–1501. IEEE (2015)Google Scholar
  56. 56.
    Bennett, J., Lanning, S.: The netflix prize. In: Proceedings of KDD Cup and Workshop, pp. 75–79. ACM (2007)Google Scholar
  57. 57.
    Nguyen, L.: A new approach for collaborative filtering based on Bayesian network inference. In: 7th International Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, pp. 475–480. IEEE (2015)Google Scholar
  58. 58.
    Nilashi, M., Bagherifard, K., Rahmani, M., Rafe, V.: A recommender system for tourism industry using cluster ensemble and prediction machine learning techniques. Comput. Ind. Eng. 109, 357–368 (2017)Google Scholar
  59. 59.
    Nilashi, M., Jannach, D., bin Ibrahim, O., Ithnin, N.: Clustering and regression-based multi-criteria collaborative filtering with incremental updates. Inf. Sci. 293, 235–250 (2015)Google Scholar
  60. 60.
    Pan, W., Chen, L.: Group Bayesian personalized ranking with rich interactions for one-class collaborative filtering. Neurocomputing 207, 501–510 (2016)Google Scholar
  61. 61.
    Park, S., Chu, W.: Pairwise preference regression for cold-start recommendation. In: Proceedings of the 3rd ACM Conference on Recommender Systems, pp. 21–28. ACM (2009)Google Scholar
  62. 62.
    Qiu, H., Liu, Y., Guo, G., Sun, Z., Zhang, J., Nguyen, H.: BPRH: Bayesian personalized ranking for heterogeneous implicit feedback. Inf. Sci. 453, 80–98 (2018)Google Scholar
  63. 63.
    Rho, W., Cho, S.: Context-aware smartphone application category recommender system with modularized Bayesian networks. In: 10th International Conference on Natural Computation, pp. 775–779. IEEE (2014)Google Scholar
  64. 64.
    Rolfsnes, T., Moonen, L., Di Alesio, S., Behjati, R., Binkley, D.: Aggregating association rules to improve change recommendation. Empirical Softw. Eng. 23(2), 987–1035 (2018)Google Scholar
  65. 65.
    Schclar, A., Tsikinovsky, A., Rokach, L., Meisels, A., Antwarg, L.: Ensemble methods for improving the performance of neighborhood-based collaborative filtering. In: Proceedings of the 3rd ACM Conference on Recommender Systems, pp. 261–264. ACM (2009)Google Scholar
  66. 66.
    Sohrabi, B., Mahmoudian, P., Raeesi, I.: A framework for improving e-commerce websites usability using a hybrid genetic algorithm and neural network system. Neural Comput. Appl. 21(5), 1017–1029 (2012)Google Scholar
  67. 67.
    Thai-Nghe, N., Drumond, L., Krohn-Grimberghe, A., Schmidt-Thieme, L.: Recommender system for predicting student performance. Procedia Comput. Sci. 1(2), 2811–2819 (2010)Google Scholar
  68. 68.
    Viktoratos, I., Tsadiras, A., Bassiliades, N.: Combining community-based knowledge with association rule mining to alleviate the cold start problem in context-aware recommender systems. Expert Syst. Appl. 101, 78–90 (2018)Google Scholar
  69. 69.
    Wang, J., Sun, J., Lin, H., Dong, H., Zhang, S.: Convolutional neural networks for expert recommendation in community question answering. Sci. China Inf. Serv. 60(11), 110–120 (2017)Google Scholar
  70. 70.
    Wang, S., Zhao, Z., Hong, X.: The research on collaborative filtering recommendation algorithm based on improved clustering processing. In: IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, pp. 1012–1015. IEEE (2015)Google Scholar
  71. 71.
    Wei, J., He, J., Chen, K., Zhou, Y., Tang, Z.: Collaborative filtering and deep learning based recommendation system for cold start items. Expert Syst. Appl. 69, 29–39 (2017)Google Scholar
  72. 72.
    Wu, H., Zhang, Z., Yue, K., Zhang, B., He, J., Sun, L.: Dual-regularized matrix factorization with deep neural networks for recommender systems. Knowl.-Based Syst. 145, 46–58 (2018)Google Scholar
  73. 73.
    Wu, J., Miao, Z.: Regression-based fusion prediction for collaborative filtering. In: International Conference on Cloud Computing and Big Data, pp. 312–319. IEEE (2013)Google Scholar
  74. 74.
    Xiaojun, L.: An Improved clustering-based collaborative filtering recommendation algorithm. Cluster Comput. 20(2), 1281–1288 (2017)Google Scholar
  75. 75.
    Xie, X., Wang, B.: Web Page recommendation via twofold clustering: considering user behavior and topic relation. Neural Comput. Appl. 29(1), 235–243 (2018)MathSciNetGoogle Scholar
  76. 76.
    Xu, Y., Zhu, Y., Shen, Y., Yu, J.: Leveraging app usage contexts for app recommendation: a neural approach. World Wide Web 1–25 (2018)Google Scholar
  77. 77.
    Yang, M., Li, Y., Zhang, Z.: Scientific articles recommendation with topic regression and relational matrix factorization. J. Zhejiang Univ. Sci. C 15(11), 984–998 (2014)Google Scholar
  78. 78.
    Zarzour, H., Al-Sharif, Z., Al-Ayyoub, M., Jararweh, Y.: A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques. In: 9th International Conference on Information and Communication Systems, pp. 102–106. IEEE (2018)Google Scholar
  79. 79.
    Zhang, C., Dai, J., Li, P., Li, Q., Luo, X.: Two-phase clustering-based collaborative filtering algorithm. In: 5th International Conference on Management of e-Commerce and e-Government, pp. 19–23. IEEE (2011)Google Scholar
  80. 80.
    Zhang, H., Ganchev, I., Nikolov, N., Ji, Z., Odroma, M.: Hybrid recommendation for sparse rating matrix: a heterogeneous information network approach. In: Proceedings of the IEEE Advanced Information Technology, Electronic and Automation Control Conference, pp. 740–744. IEEE (2017)Google Scholar
  81. 81.
    Zhang, H., Min, F., Shi, B.: Regression-based three-way recommendation. Inf. Sci. 378, 444–461 (2017)Google Scholar
  82. 82.
    Zhao, W., Zhang, H.: The improved item-based clustering collaborative filtering algorithm based on Hadoop (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sana Abida Amin
    • 1
  • James Philips
    • 2
  • Nasseh Tabrizi
    • 2
    Email author
  1. 1.Florida International UniversityMiamiUSA
  2. 2.East Carolina UniversityGreenvilleUSA

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