Abstract
In E-commerce environment, a recommender system recommend products of interest to its users. Several techniques have been proposed in the recommender systems. One of the popular techniques is collaborative filtering. Generally, the collaborative filtering technique is employed to give personalized recommendations for any given user by analyzing the past activities of the user and users similar to him/her. The memory-based and model-based collaborative filtering techniques are two different models which address the challenges such as quality, scalability, sparsity, and cold start, etc. In this paper, we conduct a review of traditional and state-of-art techniques on how they address the different challenges. We also provide the comparison results of some of the techniques.
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References
Aggarwal CC (2016) Recommender systems. Springer, Cham
Al Mamunur Rashid, SKL, Karypis G, Riedl J (2006) ClustKNN: a highly scalable hybrid model & memory-based CF algorithm. In: Proceedings of webKDD
Ambulgekar H, Pathak MK, Kokare M (2018) A survey on collaborative filtering: tasks, approaches and applications. In: Proceedings of international ethical hacking conference. Springer, pp 289–300
Amin SA, Philips J, Tabrizi N (2019) Current trends in collaborative filtering recommendation systems. In: World congress on services. Springer, pp 46–60
Bell RM, Koren Y (2007) Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In: ICDM, vol 7, pp 43–52. ACM
Grzegorzewski P, Ziembinska P (2011) Spearman’s rank correlation coefficient for vague preferences. In: International conference on FQAS. Springer, pp 342–353
Isinkaye F, Folajimi Y, Ojokoh B (2015) Recommendation systems: principles, methods and evaluation. Egyptian Inform J 16(3):261–273
Kluver D, Ekstrand MD, Konstan JA (2018) Rating-based collaborative filtering: algorithms and evaluation. In: Social information access. Springer, pp 344–390
Konstan JA, Riedl J (2012) Recommender systems: from algorithms to user experience. User Model User-Adap Inter 22(1–2):101–123
Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42:30–37
Luo X, Zhou M, Xia Y, Zhu Q (2014) An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems. IEEE Trans Ind Inform 10(2):1273–1284
Najafabadi MK, Mohamed AH, Mahrin MN (2019) A survey on data mining techniques in recommender systems. Soft Comput 23(2):627–654
Patel A, Thakkar A, Bhatt N, Prajapati P (2019) Survey and evolution study focusing comparative analysis and future research direction in the field of recommendation system specific to collaborative filtering approach. In: Information and communication technology for intelligent systems. Springer, pp 155–163 (2019)
Sarwar B, Karypis G, Konstan J, Riedl J (2002) Incremental singular value decomposition algorithms for highly scalable recommender systems. In: Fifth international conference on computer and information science, vol 27, p 28. Citeseer
Sarwar BM, Karypis G, Konstan J, Riedl J (2002) Recommender systems for large-scale ecommerce: scalable neighborhood formation using clustering. In: Proceedings of the fifth international conference on computer and information technology, vol 1, pp 291–324. ACM
Shen RP, Zhang HR, Yu H, Min F (2019) Sentiment based matrix factorization with reliability for recommendation. Expert Syst Appl 135:249–258
Silveira T, Zhang M, Lin X, Liu Y, Ma S (2019) How good your recommender system is? A survey on evaluations in recommendation. Int J MLC 10(5):813–831
Swamy MK, Reddy PK: Improving diversity performance of association rule based recommender systems. In: Proceedings of 26th international conference on database and expert systems applications, Part I. Springer, pp 499–508 (2015)
Phuong TM, Phuong ND (2019) Graph-based context-aware collaborative filtering. Expert Syst Appl 126:9–19
Valdiviezo-Diaz P, Ortega F, Cobos E, Lara-Cabrera R (2019) A collaborative filtering approach based on Naïve Bayes classifier. IEEE Access 7:108581–108592
Yao L, Xu Z, Zhou X, Lev B (2019) Synergies between association rules and collaborative filtering in recommender system: an application to auto industry. In: Data science and digital business. Springer, pp 65–80
Zhang S, Yao L, Sun A, Tay Y (2019) Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv 52(1):1–38
Zhu Y, Lin J, He S, Wang B, Guan Z, Liu H, Cai D (2019) Addressing the item cold-start problem by attribute-driven active learning. IEEE Trans KDE 32:631–644
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Aramanda, A., Md. Abdul, S., Vedala, R. (2021). A Comparison Analysis of Collaborative Filtering Techniques for Recommeder Systems. In: Kumar, A., Mozar, S. (eds) ICCCE 2020. Lecture Notes in Electrical Engineering, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-15-7961-5_9
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