Abstract
It is important for on-line retailers to better understand the interest of users for creating personalized recommendations to survive in the competitive market. Implicit details of user that is extracted from click stream data plays a vital role in making recommendations. These indicators reflect users’ items of interest. The browsing behavior, frequency of item visits, time taken to read details of an item are few measures that predict users’ interest for a particular item. After identifying these strong attributes, users are clustered on the basis of context clicks such as promotional and discounted offers and interest of the individual user is predicted for the particular context in user-context preference matrix. After clustering analysis is performed, neighborhood formation process is conducted using collaborative filtering on the basis of item category such as regular or branded items which depicts users’ interest in that particular category. Using these matrices, computational burden and processing time to generate recommendations are greatly reduced. To determine the effectiveness of proposed work, an experimental evaluation has been done which clearly depicts the better performance of the system as compared to conventional approaches.
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References
Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-85820-3_1
Jeong, B., Lee, J., Cho, H.: An iterative semi-explicit rating method for building collaborative recommender systems. Expert Syst. Appl. 36(3), 6181–6186 (2009)
Zhao, X., Niu, Z., Chen, W.: Interest before liking: two-step recommendation approaches. Knowl. Based Syst. 48, 46–56 (2013)
Cleger-Tamayo, S., Fernández-Luna, J.M., Huete, J.F.: Top-N news recommendations in digital newspapers. Knowl. Based Syst. 27, 180–189 (2012)
Kim, Y.S., Yum, B.: Recommender system based on click stream data using association rule mining. Expert Syst. Appl. 38(10), 13320–13327 (2011)
Li, Y., Tan, B.H.: Clustering algorithm of web click stream frequency pattern. J. Tianjin Univ. Sci. Technol. 3, 018 (2011)
Kim, S.C., Sung, K.J., Park, C.S., Kim, S.K.: Improvement of collaborative filtering using rating normalization. Multimed. Tools Appl. 75(9), 4957–4968 (2016)
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, pp. 285–295. ACM (2001)
Kim, S.C., Sung, K.J., Park, C.S., Kim, S.K.: Improvement of collaborative filtering using rating normalization. Multimed. Tools Appl. 75(9), 4957–4968 (2016)
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, pp. 285–295. ACM (2001)
Kim, H.N., Ji, A.T., Ha, I., Jo, G.S.: Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation. Electron. Commer. Res. Appl. 9(1), 73–83 (2010)
Park, Y.J., Chang, K.N.: Individual and group behavior-based customer profile model for personalized product recommendation. Expert Syst. Appl. 36(2), 1932–1939 (2009)
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Ghauth, K.I., Abdullah, N.A.: Measuring learner’s performance in e-learning recommender systems. Australas. J. Educ. Technol. 26(6), 764–774 (2010)
Ghazanfar, M.A.: Experimenting switching hybrid recommender systems. Intell. Data Anal. 19(4), 845–877 (2015)
Ben-Shimon, D., Tsikinovsky, A., Friedmann, M., Shapira, B., Rokach, L., Hoerle, J.: Recsys challenge 2015 and the yoochoose dataset. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 357–358. ACM (2015)
Gupta, S., Dixit, V.S.: Scalable online product recommendation engine based on implicit feature extraction domain. J. Intell. Fuzzy Syst. 34(3), 1503–1510 (2018)
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Gupta, S., Dixit, V.S. (2018). A Business Intelligent Framework to Evaluate Prediction Accuracy for E-Commerce Recommenders. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10963. Springer, Cham. https://doi.org/10.1007/978-3-319-95171-3_22
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