Predicting online user purchase behavior based on browsing history

Yunghui Chu, Hui Kuo Yang, Wen Chih Peng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Recently, people tend to purchase through websites. This change allows e-commerce sites to collect user behavior data from web logs. E-commerce marketing forces usually make use of such data to come up with subsequent promotional campaign to drive more traffic, and converting into paying customers. In this paper we consider a special kind of e-commerce companies which sell products with similar property and usually at a high price. Therefore, the recommendation becomes less important than prediction of items(if any) bought. We want to discover potential buyers and deliver ads or even coupons to them, expecting them to be real buyers. In this paper, we model the buying behaviors from clicking records with patterns extracted using feature engineering approach. Our solution was to model two kinds of browsing behaviors, namely hesitant and impulsive respectively. In the model, we define some interaction features from click-streams which uncover users' purchase intention with the product pages, how long the user stays on that page, and then build a model which can predict users' preference. Experimental results on a real dataset from an e-commerce company demonstrate the effectiveness of the proposed method. The approaches in our work can be used to model user purchasing intent and applied to e-commerce sites which sell high-end products.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 35th International Conference on Data Engineering Workshops, ICDEW 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages185-192
Number of pages8
ISBN (Electronic)9781728108902
DOIs
StatePublished - Apr 2019
Event35th IEEE International Conference on Data Engineering Workshops, ICDEW 2019 - Macau, China
Duration: 8 Apr 201912 Apr 2019

Publication series

NameProceedings - 2019 IEEE 35th International Conference on Data Engineering Workshops, ICDEW 2019

Conference

Conference35th IEEE International Conference on Data Engineering Workshops, ICDEW 2019
CountryChina
CityMacau
Period8/04/1912/04/19

Keywords

  • Browsing logs
  • E-commerce
  • Interactive features
  • Purchase intention
  • Recurrent neural network

Fingerprint Dive into the research topics of 'Predicting online user purchase behavior based on browsing history'. Together they form a unique fingerprint.

Cite this