A deep recurrent neural network approach to learn sequence similarities for user-identification-Reference-Cited by-同舟云学术

A deep recurrent neural network approach to learn sequence similarities for user-identification

Published:2022-04 Issue: Volume:155 Page:113718
ISSN:0167-9236
Container-title:Decision Support Systems
language:en
Short-container-title:Decision Support Systems

Author:

Vamosi Stefan,Reutterer Thomas^ORCID,Platzer Michael^ORCID

Funder

FFG

Publisher

Elsevier BV

Subject

Information Systems and Management,Arts and Humanities (miscellaneous),Developmental and Educational Psychology,Information Systems,Management Information Systems

Reference48 articles.

1. Click here for internet insight: advances in clickstream data analysis in marketing;Bucklin;J. Int. Market.,2009

2. Analyzing the browsing basket: a latent interests-based segmentation tool;Schröder;J. Int. Market.,2019

3. Understanding user behavior in spotify;Zhang,2013

4. Guess who rated this movie: identifying users through subspace clustering;Zhang,2012

5. An analysis of mobile banking user behavior using customer segmentation;Noori;Int. J. Global Busin.,2015

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