[Objective] This research characterizes users’ browsing patterns, aiming to predict their purchasing decisions on mobile shopping applications. [Methods] First, we mapped the request parameters of the logs with users’ information behavior types. Then, we used logistic binary regression and C&R decision tree techniques to establish models to predict the buying decisions. The data set included 3,923,429 lines of server logs generated by 290 heavy users of a popular mobile shopping app in March 2015. [Results] We found that the frequency of users’ browsing behaviors was stable during the weekdays and reached its peak every night before bedtime. Users paid much attention to product details and those with deeper browsing behaviors are more likely to read introduction to the shop and share related information. The number of views was in line with the power-law distribution and 90% of the merchandise was checked less than 16 times. We also found that goods viewed by 9 times and placed in the carts were most likely to be bought. There was a positive correlation between the purchases of goods and the numbers of views or sharing of the item and the shop. The C&R decision tree model’s prediction accuracy was slightly higher than that of the Logistic binary regression model. However, the former’s variable types were far less than the latter. [Limitations] Logs cannot fully reflect all users’ behaviors, which lead to some ambiguity of our analysis. The conclusion might not tell the whole story since the logs were generated by heavy users in one month. [Conclusions] The pattern of user browsing and buying behaviors could be used to enhance their experience of the mobile shopping applications. Logistic binary regression might better predict users’ buying decisions than the C&R decision trees model.
(Lu Minling, Cao Yuzhi, Lu Yaobin.A Study on Consumers’ Adoption of Mobile Shopping Services from a Perspective of Features in the Mobile Environment[J].Journal of Intelligence, 2012, 31(9): 202-207.)
(Wang Zhijin, Han Zhengbiao, Zhou Peng.The Formation Mechanism of the Customer Information Search Behavior in E-commerce Website[J]. Library & Information, 2011(3): 12-16.)
doi: 10.3969/j.issn.1003-6938.2011.03.004
(Yuan Xingfu, Zhang Pengyi, Wang Jun.“State-Behavior” Modeling and Its Application in Analyzing Product Information Seeking Behavior of E-commerce Websites Users[J]. New Technology of Library and Information Service, 2015(6): 93-100.)
(Yuan Xingfu, Zhang Pengyi, Liu Honglian, et al.Modeling E-commerce User Session Behaviors Based on Click-through Sequences[J]. Library and Information Service, 2015, 59(1): 119-126.)
doi: 10.13266/j.issn.0252-3116.2015.01.016
[7]
Farag N I, Smith M D, Krishnan M S.The Consumer Online Purchase Decision: A Model of Consideration Set Formation and Buyer Conversion Rate Across Market Leaders and Market Followers[C]//Proceedings of the International Conference on Information Systems. 2003: 283-295.
(Wang Jun, Li Xin.Research on the Impact of Self-efficacy on Network Information Seeking Behavior[J]. Library and Information Service, 2014, 58(14): 110-114.)
doi: 10.13266/j.issn.0252-3116.2014.14.016
(Xu Yingnan.Analysis of Commodity Parameters Browsing Preference in Consumer’s Online Shopping Decision-making——Taking Digital Camera for Example[J]. New Technology of Library and Information Service, 2012(12): 52-57.)
(Liu Honglian, Zhang Pengyi, Wang Jun.Product Information Seeking Behavior of Multi-session Online Shopping Tasks[J]. Library and Information Service, 2015, 59(14): 117-125.)
doi: 10.13266/j.issn.0252-3116.2015.14.017
(Liu Honglian, Zhang Pengyi, Wang Jun.Multi-session Product Information Seeking Behaviors, Motivation, and Influencing Factors[J]. New Technology of Library and Information Service, 2016 (4): 1-7.)
[13]
Ji J, Liu C, Sha Z, et al.Personalized Recommendation Based on a Multilevel Customer Model[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2005, 19(7): 895-916.
doi: 10.1142/S021800140500437X
(Ji Zheng.Recommendation Technology Based on User’s Interest Model for the E-commerce Site[J].Library and Information Service, 2010, 54(16): 138-140.)
[15]
Qiu J, Lin Z, Li Y.Predicting Customer Purchase Behavior in the E-commerce Context[J]. Electronic Commerce Research, 2015, 15(4): 427-452.
doi: 10.1007/s10660-015-9191-6
[16]
Li Q, Gu M, Zhou K, et al.Multi-Classes Feature Engineering with Sliding Window for Purchase Prediction in Mobile Commerce[C]// Proceedings of the IEEE International Conference on Data Mining Workshop, 2016.
[17]
Gupta R, Pathak C.A Machine Learning Framework for Predicting Purchase by Online Customers Based on Dynamic Pricing[J]. Procedia Computer Science, 2014, 36: 599-605.
doi: 10.1016/j.procs.2014.09.060
(Zeng Xianyu, Liu Qi, Zhao Hongke, et al.Online Consumptions Prediction via Modeling User Behaviors and Choices[J].Journal of Computer Research and Development, 2016, 53(8): 1673-1683.)
(Wu Guohua, Pan Dehui.Analyzing the Main Elements of Customer Purchase Behavior and Predicting the Probability of Customer Repurchase[J]. Journal of Industrial Engineering, 2005, 19(1): 104-107.)
(Zhang Kuo, Li Guihua, Li Yanfei.The Development of Forecasting Model on Consumers Life Insurance Purchasing by Discriminant Analysis and Logistic Regression[J]. Journal of Applied Statistics and Management, 2011, 30(2): 291-298.)
(Mao Qianren, Wang Chaobin.Data Mining on College Students’ Laptop Purchase Behavior Based on Decision Tree C4.5[J].Journal of Chongqing University of Technology: Natural Science, 2015, 29(2): 76-81.)
(Feng Xiurong, Leng Jing, Liu Hai.Application of C5.0 Decision Tree Algorithm to Loss of Mobile Reading Users[J]. Journal of Beijing Information Science & Technology University, 2016, 31(1): 84-89.)
doi: 10.16508/j.cnki.11-5866/n.2016.01.017
(Zhu Tong, Liu Qiqun, Ru Liyun, et al.Long Query User Satisfaction Analysis Based on User Behaviors[J]. PR & AI, 2012, 25(3): 469-474.)
doi: 10.3969/j.issn.1003-6059.2012.03.016
(Zhang Chenyang, Yu Rong, Zhang Haochuan.Analysis and Prediction of Willingness to Pay for Mobile Networks Users Based on Decision Tree[J]. Wireless Internet Technology, 2017(15): 21-23.)
(Yang Jieming, Yan Xin, Qu Zhaoyang, et al.Under-sampling Technique Based on Data Density Distribution[J]. Application Research of Computers, 2016, 33(10): 2997-3000.)
doi: 10.3969/j.issn.1001-3695.2016.10.029
(Huang Weilai, Pan Xiaobo.The Usefulness Model of Online Product Reviews: The Extensive Information Adoption Model Bringing into the Application Environment[J]. Library and Information Service, 2014, 58(S1): 141-151.)
(Yun Xiaofeng.An Empirical Research on the Determinants of Consumers’ Online Shopping Cart Abandonment[J]. Library and Information Service, 2011, 55(2): 139-142.)