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- W3089652371 abstract "Improvements to customer experience give companies a competitive advantage, as understanding customers' behaviors allows e-commerce companies to enhance their marketing strategies by means of recommendation techniques and the customization of products and services. This is not a simple task, and it becomes more difficult when working with anonymous sessions since no historical information of the user can be applied. In this article, analysis and clustering of the clickstreams of past anonymous sessions are used to synthesize a prediction model based on a neural network. The model allows for prediction of a user's profile after a few clicks of an online anonymous session. This information can be used by the e-commerce's decision system to generate online recommendations and better adapt the offered services to the customer's profile." @default.
- W3089652371 created "2020-10-08" @default.
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- W3089652371 date "2020-01-01" @default.
- W3089652371 modified "2023-09-30" @default.
- W3089652371 title "Log-Based Session Profiling and Online Behavioral Prediction in E–Commerce Websites" @default.
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- W3089652371 doi "https://doi.org/10.1109/access.2020.3024649" @default.
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