Use Logistic Regression to Predict User’ Behaviors

Abstract:

Accurately capturing user behaviors over time is a great practical challenge in recommender system. Recently there is an Alibaba competition, which is to predict users’ purchase behave according to users’ previous four months behaviors, including click, purchase, add to collection and add to cart. We find that the brand is of crucial for user and click have a great impact for users. Besides, 1 / 5 users may buy according to previous interest. 4 / 5 users may choose other brands. While we focus on the predicting users for those brands which they have some behaviors. We extract some features, such as the number of click, the most last time for click, the number of purchase, the most last time for purchase. Then we use assemble model to predict users’ behaviors, which is better than other models.

• Periodical: Applied Mechanics and Materials (Volumes 651-653)
• Pages: 1695-1698
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.651-653.1695
• Online since: September 2014
• Authors: Bing Chuan Long Huang*, Yang Xiang, Zhen Hua Huang
• Keywords: Assemble Mode, Brands Preference, Users Behavior

References

[1] N. N. Liu and Q. Yang. Eigenrank: a ranking-oriented approach to collaborative filtering. In SIGIR '08, pages 83–90, (2008).

[2] F. Ricci and Q. N. Nguyen. Acquiring and revising preferences in a critique-based mobile recommender system. IEEE Intelligent Systems, 22(3): 22–29, (2007).

DOI: 10.1109/mis.2007.43

[3] J. Sun, C. Faloutsos, S. Papadimitriou, and P. S. Yu. Graphscope: parameter-free mining of large time-evolving graphs. In KDD '07, pages 687–696, (2007).

DOI: 10.1145/1281192.1281266

[4] A. T¨scher and M. Jahrer. The bigchaos solution to the netflix prize 2008. Technical report, (2008).

[5] C. E. Larsen, R. Trip, and C.R. Johnson, Methods for procedures related to the electrophysiology of the heart. U.S. Patent 5, 529, 067, Jun 25 (1995).

[6] S. Baluja, R. Seth, D. Sivakumar, Y. Jing, J. Yagnik,S. Kumar, D. Ravichandran, and M. Aly. Video suggestion and discovery for youtube: taking random walks through the view graph. In WWW '08, pages 895–904, (2008).

DOI: 10.1145/1367497.1367618

[7] Y. Ding and X. Li. Time weight collaborative filtering. In CIKM '05, pages 485–492, (2005).

[8] M. Gori and A. Pucci. Research paper recommender systems: A random-walk based approach. In WI '06, pages 778–781, (2006).

DOI: 10.1109/wi.2006.149

[9] Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In ICDM '08, pages 263–272, (2008).

[10] M. Jamali and M. Ester. Trustwalker: a random walk model for combining trust-based and item-based recommendation. In KDD '09, pages 397–406, (2009).

DOI: 10.1145/1557019.1557067

[11] R. J¨schke, L. Marinho, A. Hotho, a L. Schmidt-Thieme, and G. Stumme. Tag recommendations in social bookmarking systems. AICommun., 21(4): 231–247, (2008).

DOI: 10.3233/aic-2008-0438

[12] N. Lathia, S. Hailes, and L. Capra. Temporal collaborative filtering with adaptive neighbourhoods. In SIGIR '09, pages 796–797, (2009).

DOI: 10.1145/1571941.1572133

[13] M. Li, B. M. Dias, I. Jarman, W. El-Deredy, and P. J. Lisboa. Grocery shopping recommendations based on basket-sensitive random walk. In KDD '09, pages 1215–1224, (2009).

DOI: 10.1145/1557019.1557150

[14] Y. Li and J. Tang. Expertise search in a time-varying social network. In WAIM '08, pages 293–300, (2008).

[15] G. Linden, B. Smith, and J. York. Amazon. comrecommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1): 76–80, (2003).

DOI: 10.1109/mic.2003.1167344