research-article

Bitwise parallel association rule mining for web page recommendation

Authors:

Carson K. Leung,

Adam G. M. PazdorAuthors Info & Claims

WI '17: Proceedings of the International Conference on Web Intelligence

Pages 662 - 669

https://doi.org/10.1145/3106426.3106542

Published: 23 August 2017 Publication History

Abstract

For many real-life web applications, web surfers would like to get recommendation on which collections of web pages that would be interested to them or that they should follow. In order to discover this information and make recommendation, data mining---and specially, association rule mining or web mining---is in demand. Since its introduction, association rule mining has drawn attention of many researchers. Consequently, many association rule mining algorithms have been proposed for finding interesting relationships---in the form of association rules---among frequently occurring patterns. These algorithms include level-wise Apriori-based algorithms, tree-based algorithms, hyperlinked array structure based algorithms, and vertical mining algorithms. While these algorithms are popular, they suffer from some drawbacks. Moreover, as we are living in the era of big data, high volumes of a wide variety of valuable data of different veracity collected at a high velocity post another challenges to data science and big data analytics. To deal with these big data while avoiding the drawbacks of existing algorithms, we present a bitwise parallel association rule mining system for web mining and recommendation in this paper. Evaluation results show the effectiveness and practicality of our parallel algorithm---which discovers popular pages on the web, which in turn gives the web surfers recommendation of web pages that might be interested to them---in real-life web applications.

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Cited By

Hryhoruk CLeung C(2022)Web Mining from Interpretable Compressed Representation of Sparse Web2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT55865.2022.00097(620-627)Online publication date: Nov-2022
https://doi.org/10.1109/WI-IAT55865.2022.00097
Chhatwal GDeepak G(2022)IEESWPR: An Integrative Entity Enrichment Scheme for Socially Aware Web Page RecommendationData Science and Security10.1007/978-981-19-2211-4_21(239-249)Online publication date: 2-Jul-2022
https://doi.org/10.1007/978-981-19-2211-4_21
Wu ZLi CCao JGe Y(2020)On Scalability of Association-rule-based RecommendationACM Transactions on the Web10.1145/339820214:3(1-21)Online publication date: 21-Jun-2020
https://dl.acm.org/doi/10.1145/3398202
Show More Cited By

Index Terms

Bitwise parallel association rule mining for web page recommendation
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Social recommendation
2. Information systems

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Published In

cover image ACM Conferences

WI '17: Proceedings of the International Conference on Web Intelligence

August 2017

1284 pages

ISBN:9781450349512

DOI:10.1145/3106426

Conference Chair:
Amit Sheth
Wright State University GuoROLE@GENERAL CHAIR
,
General Chairs:
Axel Ngonga
Leipzig University, Germany
,
yin Wang
Chongqing University of Posts and Telecommunications, China
,
Elizabeth Chang
The University of New South Wales, Australia
,
Dominik Ślęzak
Infobright Inc. & University of Warsaw, Poland
,
Bogdan Franczyk
Leipzig University, Germany
,
Program Chairs:
Rainer Alt
Leipzig University, Germany
,
Xiaohui Tao
University of Southern Queensland, Australia

Copyright © 2017 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 the author(s) 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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SIGAI: ACM Special Interest Group on Artificial Intelligence
TCII: IEEE Computer Society Technical Committee on Intelligent Informatics
Web Intelligence Consortium

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 August 2017

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Natural Sciences and Engineering Research Council of Canada

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WI '17

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SIGAI
TCII

WI '17: International Conference on Web Intelligence 2017

August 23 - 26, 2017

Leipzig, Germany

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WI '17 Paper Acceptance Rate 118 of 178 submissions, 66%;

Overall Acceptance Rate 118 of 178 submissions, 66%

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Cited By

Hryhoruk CLeung C(2022)Web Mining from Interpretable Compressed Representation of Sparse Web2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT55865.2022.00097(620-627)Online publication date: Nov-2022
https://doi.org/10.1109/WI-IAT55865.2022.00097
Chhatwal GDeepak G(2022)IEESWPR: An Integrative Entity Enrichment Scheme for Socially Aware Web Page RecommendationData Science and Security10.1007/978-981-19-2211-4_21(239-249)Online publication date: 2-Jul-2022
https://doi.org/10.1007/978-981-19-2211-4_21
Wu ZLi CCao JGe Y(2020)On Scalability of Association-rule-based RecommendationACM Transactions on the Web10.1145/339820214:3(1-21)Online publication date: 21-Jun-2020
https://dl.acm.org/doi/10.1145/3398202
Leung CJiang FZhang Y(2020)Explainable Machine Learning and Mining of Influential Patterns from Sparse Web2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WIIAT50758.2020.00128(829-836)Online publication date: Dec-2020
https://doi.org/10.1109/WIIAT50758.2020.00128
Singh SLeung CHamilton J(2020)Analytics of Similar-Sounding Names from the Web with Phonetic Based Clustering2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WIIAT50758.2020.00087(580-585)Online publication date: Dec-2020
https://doi.org/10.1109/WIIAT50758.2020.00087
Leung C(2020)Big Data Computing and Mining in a Smart WorldBig Data Analyses, Services, and Smart Data10.1007/978-981-15-8731-3_2(15-27)Online publication date: 11-Sep-2020
https://doi.org/10.1007/978-981-15-8731-3_2
Leung CDesai BAnagnostopoulos DManolopoulos YNikolaidou M(2019)Pattern mining for knowledge discoveryProceedings of the 23rd International Database Applications & Engineering Symposium10.1145/3331076.3331099(1-5)Online publication date: 10-Jun-2019
https://dl.acm.org/doi/10.1145/3331076.3331099
Leung CJiang FSouza J(2018)Web Page Recommendation from Sparse Big Web Data2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)10.1109/WI.2018.00-32(592-597)Online publication date: Dec-2018
https://doi.org/10.1109/WI.2018.00-32
Leung CHoi CPazdor AWodi BCuzzocrea A(2018)Privacy-Preserving Frequent Pattern Mining from Big Uncertain Data2018 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2018.8622260(5101-5110)Online publication date: Dec-2018
https://doi.org/10.1109/BigData.2018.8622260
Braun PCuzzocrea ALeung CPazdor ASouza J(2018)Item-centric mining of frequent patterns from big uncertain dataProcedia Computer Science10.1016/j.procs.2018.08.075126(1875-1884)Online publication date: 2018
https://doi.org/10.1016/j.procs.2018.08.075
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