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Feature Generation and Feature Engineering for Sequences
Published in Guozhu Dong, Huan Liu, Feature Engineering for Machine Learning and Data Analytics, 2018
Guozhu Dong, Lei Duan, Jyrki Nummenmaa, Peng Zhang
Distinguishing sequence patterns, discovered from two sets of sequences, are useful as sequence features to characterize a set of sequences and distinguish the set from other sets of sequences. (These are related to contrast patterns [6] and emerging patterns [7].) The above can also be defined in terms of one interval vs. other intervals instead of one set vs. other sets. We may, e.g., want to find distinguishing patterns in clickstream data to analyze the differences between customers who click an advertisement and customers who do not.
Usability Testing
Published in Julie A. Jacko, The Human–Computer Interaction Handbook, 2012
Online tests provide the potential for additional measures. Click stream data can show pages visited, page transitions, and how much time users spend on pages or key areas of pages (Albert, Tullis, and Tedesco 2010). For example, looking at pages visited during failed tasks can provide additional clues about design flaws. The larger sample sizes with online tests also make it possible to break the total population of participants into smaller segments, which is usually impossible with the small samples used in moderated tests.
Data Mining and Security
Published in Bhavani Thuraisngham, Murat Kantarcioglu, Latifur Khan, Secure Data Science, 2022
Bhavani Thuraisngham, Murat Kantarcioglu, Latifur Khan
In the case of web transactions, we use association rules to discover navigational patterns among users. This would help to cache a page in advance and reduce the loading time of a page. Also, discovering a pattern of navigation helps in personalization. Transactions are captured from the clickstream data captured in web server logs.
Research on assistant decision-makings of E-commerce platform with refinement tools
Published in Journal of Control and Decision, 2021
Hongwei Liu, Mingjun Zhan, Hongming Gao, Hui Zhu, Ruichao He
In contrast to brick and mortar stores, the online storekeepers can no longer communicate and interact with the buyers face to face, which accounts for the predicament of online consumer decision-making analysis till the accessibility and availability of clickstream data. Existing literature defines clickstream data as the activities made by a user when navigating the website online (Bucklin & Sismeiro, 2009). The data records every footprint a user left and thereby this user’s shopping process is able to be traced, which indicates that clickstream data contains fruitful, potential and more detailed information for consumer behaviour analysis (Suchacka & Chodak, 2017). The research on clickstream data has been developed on aspects like website usage and navigation, advertising as well as online shopping behaviour, and it was also found to play a persuasion effect on purchase conversion as a medium role (Bucklin & Sismeiro, 2009; Liu et al., 2018; Liu et al., 2019). Consumers are nowadays more and more knowledgeable about the usage of e-commerce platforms, Wang and Li (2018) noticed the phenomenon and took the platform’s service as a decision variable, the result showed that logistic cost has an impact on the decision-making process. However, there should be sub-decisions for gathering information before consumers make their final decision, which would influence the interface display of the platform and their subsequent decision.