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Intelligent web usage clustering based recommender system

Author:

Shafiq AlamAuthors Info & Claims

RecSys '11: Proceedings of the fifth ACM conference on Recommender systems

Pages 367 - 370

https://doi.org/10.1145/2043932.2044008

Published: 23 October 2011 Publication History

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Abstract

Our work focuses on tackling the problem of efficiency and accuracy of web usage clustering for recommender systems. Accurate analysis and preprocessing of web usage data and efficient web usage clustering are the key factors that influence the development of clustering based implicit recommender system. We propose an analysis and preprocessing model to tackle the poor quality of web usage data. To address the problem of efficient web usage clustering, we propose a Particle Swarm Optimization (PSO) based clustering approach. Having shown our PSO based clustering performs well; we extend it for mining the usage behavior of web users. We select Java API (Application Programming Interface) documentation usage data as a case study for our recommender system.

References

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S. Alam, G. Dobbie, and P. Riddle. Exploiting swarm behaviour of simple agents for clustering web users session's data. In L. Cao, editor, Data Mining and Multi-agent Integration, pages 61--75. Springer US, 2009.

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Cited By

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Wang PZuo XGuo CLi RZhao XLuo C(2017)A multiobjective genetic algorithm based hybrid recommendation approach2017 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2017.8285336(1-6)Online publication date: Nov-2017
https://doi.org/10.1109/SSCI.2017.8285336
Ansari ZSattar SBabu A(2017)A fuzzy neural network based framework to discover user access patterns from web log dataAdvances in Data Analysis and Classification10.1007/s11634-015-0228-411:3(519-546)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s11634-015-0228-4
Alam SDobbie GKoh YRiddle P(2014)Web bots detection using Particle Swarm Optimization based clustering2014 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2014.6900644(2955-2962)Online publication date: Jul-2014
https://doi.org/10.1109/CEC.2014.6900644
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Index Terms

Intelligent web usage clustering based recommender system
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
  2. Information systems applications
    1. Data mining
      1. Clustering

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Information

Published In

RecSys '11: Proceedings of the fifth ACM conference on Recommender systems

October 2011

414 pages

ISBN:9781450306836

DOI:10.1145/2043932

General Chairs:
Bamshad Mobasher
DePaul University, USA
,
Robin Burke
DePaul University, USA
,
Program Chairs:
Dietmar Jannach
TU Dortmund, Germany
,
Gediminas Adomavicius
University of Minnesota, USA

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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New York, NY, United States

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Published: 23 October 2011

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RecSys '11

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SIGCHI

RecSys '11: Fifth ACM Conference on Recommender Systems

October 23 - 27, 2011

Illinois, Chicago, USA

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Wang PZuo XGuo CLi RZhao XLuo C(2017)A multiobjective genetic algorithm based hybrid recommendation approach2017 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2017.8285336(1-6)Online publication date: Nov-2017
https://doi.org/10.1109/SSCI.2017.8285336
Ansari ZSattar SBabu A(2017)A fuzzy neural network based framework to discover user access patterns from web log dataAdvances in Data Analysis and Classification10.1007/s11634-015-0228-411:3(519-546)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s11634-015-0228-4
Alam SDobbie GKoh YRiddle P(2014)Web bots detection using Particle Swarm Optimization based clustering2014 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2014.6900644(2955-2962)Online publication date: Jul-2014
https://doi.org/10.1109/CEC.2014.6900644
Alam SDobbie GKoh YRiddle PUr Rehman S(2014)Research on particle swarm optimization based clustering: A systematic review of literature and techniquesSwarm and Evolutionary Computation10.1016/j.swevo.2014.02.00117(1-13)Online publication date: Aug-2014
https://doi.org/10.1016/j.swevo.2014.02.001
Kundra KKaur USingh D(2014)Efficient Web Log Mining and Navigational Prediction with EHPSO and Scaled Markov ModelComputational Intelligence in Data Mining - Volume 310.1007/978-81-322-2202-6_48(529-543)Online publication date: 12-Dec-2014
https://doi.org/10.1007/978-81-322-2202-6_48
Alam SDobbie GKoh YRiddle P(2013)Clustering heterogeneous web usage data using Hierarchical Particle Swarm Optimization2013 IEEE Symposium on Swarm Intelligence (SIS)10.1109/SIS.2013.6615172(147-154)Online publication date: Apr-2013
https://doi.org/10.1109/SIS.2013.6615172
Alam SDobbie GRiddle PKoh Y(2013)Analysis of Web Usage Data for Clustering Based Recommender SystemTrends in Practical Applications of Agents and Multiagent Systems10.1007/978-3-319-00563-8_21(171-179)Online publication date: 2013
https://doi.org/10.1007/978-3-319-00563-8_21
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
Alam SDobbie GRiddle PKoh Y(2012)Hierarchical PSO clustering based recommender system2012 IEEE Congress on Evolutionary Computation10.1109/CEC.2012.6256652(1-8)Online publication date: Jun-2012
https://doi.org/10.1109/CEC.2012.6256652

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Web usage mining based recommender systems using implicit heterogeneous data: A Particle Swarm Optimization based clustering approach

Towards recommender system using particle swarm optimization based web usage clustering

Improving Accuracy of Recommender System by Item Clustering

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