research-article

Recommender systems: incremental clustering on web log data

Authors:

Saeed R. Aghabozorgi,

Teh Ying WahAuthors Info & Claims

ICIS '09: Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human

Pages 812 - 818

https://doi.org/10.1145/1655925.1656073

Published: 24 November 2009 Publication History

Abstract

Nowadays, recommendation systems are definitely a necessity in the websites not just an auxiliary feature, especially for commercial websites and web sites with large information services. Recommendation systems use models constructed by applying statistical and data mining approaches on derived data from websites. In this paper we propose a new hybrid approach that leverages usage data and data domain of website to construct a recommendation model. A data mining model will be created by applying clustering algorithm, and then the model is adjusted by statistical approach based on the change of behavior of users or data domain of website periodically. We believe that by this novel approach the problem of inaccuracy of conventional usage data models partly due to slowly change of behavior of users or data domain of websites will be solved.

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Cited By

Zhu YLi HLiao YWang BGuan ZLiu HCai D(2017)What to do next: modeling user behaviors by time-LSTMProceedings of the 26th International Joint Conference on Artificial Intelligence10.5555/3172077.3172393(3602-3608)Online publication date: 19-Aug-2017
https://dl.acm.org/doi/10.5555/3172077.3172393
Jannach DLudewig MLerche L(2017)Session-based item recommendation in e-commerceUser Modeling and User-Adapted Interaction10.1007/s11257-017-9194-127:3-5(351-392)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s11257-017-9194-1
Vasconcelos LSantos RBaldochi L(2016)Exploiting Client Logs to Support the Construction of Adaptive e-Commerce Applications2016 IEEE 13th International Conference on e-Business Engineering (ICEBE)10.1109/ICEBE.2016.036(164-169)Online publication date: Nov-2016
https://doi.org/10.1109/ICEBE.2016.036
Show More Cited By

Index Terms

Recommender systems: incremental clustering on web log data
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Other conferences

ICIS '09: Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human

November 2009

1479 pages

ISBN:9781605587103

DOI:10.1145/1655925

Conference Chairs:
Sungwon Sohn
ETRI, Korea
,
Kyhyun Um
Dongguk University, Korea
,
Franz I. S. Ko
Dongguk University, Korea/IBC, UK
,
Kae Dal Kwack
Hanyang University, Korea
,
Jong Hyung Lee
Gumi Electronics & Information Technology Research Institute, Korea
,
Gang Kou
University of Electronic Science and Technology of China, China
,
Kiyoshi Nakamura
Waseda University, Japan
,
ACM Fong
Nanyang Technological University, Singapore
,
Patrick Ma
The Hong Kong Polytechnic University, Hongkong
,
Ling Chen
Zhejiang University, China
,
Soonwook Hwang
KISTI, Korea
,
Kyungeun Cho
Dongguk University, Korea
,
Shigeo Kawata
Utsunomiya University, Japan

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 24 November 2009

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ICIS '09: The 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human

November 24 - 26, 2009

Seoul, Korea

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Cited By

Zhu YLi HLiao YWang BGuan ZLiu HCai D(2017)What to do next: modeling user behaviors by time-LSTMProceedings of the 26th International Joint Conference on Artificial Intelligence10.5555/3172077.3172393(3602-3608)Online publication date: 19-Aug-2017
https://dl.acm.org/doi/10.5555/3172077.3172393
Jannach DLudewig MLerche L(2017)Session-based item recommendation in e-commerceUser Modeling and User-Adapted Interaction10.1007/s11257-017-9194-127:3-5(351-392)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s11257-017-9194-1
Vasconcelos LSantos RBaldochi L(2016)Exploiting Client Logs to Support the Construction of Adaptive e-Commerce Applications2016 IEEE 13th International Conference on e-Business Engineering (ICEBE)10.1109/ICEBE.2016.036(164-169)Online publication date: Nov-2016
https://doi.org/10.1109/ICEBE.2016.036
Jannach DLerche LJugovac MWerthner HZanker MGolbeck JSemeraro G(2015)Adaptation and Evaluation of Recommendations for Short-term Shopping GoalsProceedings of the 9th ACM Conference on Recommender Systems10.1145/2792838.2800176(211-218)Online publication date: 16-Sep-2015
https://dl.acm.org/doi/10.1145/2792838.2800176
Endert AHossain MRamakrishnan NNorth CFiaux PAndrews C(2014)The human is the loopJournal of Intelligent Information Systems10.1007/s10844-014-0304-943:3(411-435)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1007/s10844-014-0304-9
López-Nores MBlanco-Fernández YPazos-Arias JMartín-Vicente M(2013)Context-Aware Recommender Systems Influenced by the Users’ Health-Related DataUser Modeling and Adaptation for Daily Routines10.1007/978-1-4471-4778-7_6(153-173)Online publication date: 22-Jan-2013
https://doi.org/10.1007/978-1-4471-4778-7_6
Hossain MOjili PGrimm CMuller RWatson LRamakrishnan N(2012)Scatter/Gather ClusteringIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2012.25818:12(2829-2838)Online publication date: 1-Dec-2012
https://dl.acm.org/doi/10.1109/TVCG.2012.258

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