Article

Using Sequential and Non-Sequential Patterns in Predictive Web Usage Mining Tasks

Authors:

Bamshad Mobasher,

Honghua Dai,

Tao Luo,

Miki NakagawaAuthors Info & Claims

ICDM '02: Proceedings of the 2002 IEEE International Conference on Data Mining

Page 669

Published: 09 December 2002 Publication History

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Abstract

We describe an efficient framework for Web personalizationbased on sequential and non-sequential pattern discov-eryfrom usage data. Our experimental results performedon real usage data indicate that more restrictive patterns,such as contiguous sequential patterns (e.g., frequent navigationalpaths) are more suitable for predictive tasks, suchas Web prefetching, which involve predicting which item isaccessed next by a user), while less constrained patterns,such as frequent itemsets or general sequential patterns aremore effective alternatives in the context of Web personalizationand recommender systems.

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Liu BZhang HKong LNiu D(2021)Factorizing Historical User Actions for Next-Day Purchase PredictionACM Transactions on the Web10.1145/346822716:1(1-26)Online publication date: 28-Sep-2021
https://dl.acm.org/doi/10.1145/3468227
Zhang WChen ZZha HWang J(2021)Learning from Substitutable and Complementary Relations for Graph-based Sequential Product RecommendationACM Transactions on Information Systems10.1145/346430240:2(1-28)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1145/3464302
Bai TZou LZhao WDu PLiu WNie JWen JPiwowarski BChevalier MGaussier EMaarek YNie JScholer F(2019)CTRecProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331199(675-684)Online publication date: 18-Jul-2019
https://dl.acm.org/doi/10.1145/3331184.3331199
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Index Terms

Using Sequential and Non-Sequential Patterns in Predictive Web Usage Mining Tasks
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
  2. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Published In

ICDM '02: Proceedings of the 2002 IEEE International Conference on Data Mining

December 2002

ISBN:0769517544

Publisher

IEEE Computer Society

United States

Publication History

Published: 09 December 2002

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Liu BZhang HKong LNiu D(2021)Factorizing Historical User Actions for Next-Day Purchase PredictionACM Transactions on the Web10.1145/346822716:1(1-26)Online publication date: 28-Sep-2021
https://dl.acm.org/doi/10.1145/3468227
Zhang WChen ZZha HWang J(2021)Learning from Substitutable and Complementary Relations for Graph-based Sequential Product RecommendationACM Transactions on Information Systems10.1145/346430240:2(1-28)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1145/3464302
Bai TZou LZhao WDu PLiu WNie JWen JPiwowarski BChevalier MGaussier EMaarek YNie JScholer F(2019)CTRecProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331199(675-684)Online publication date: 18-Jul-2019
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Massimo DRicci FPera SEkstrand MAmatriain XO'Donovan J(2018)Harnessing a generalised user behaviour model for next-POI recommendationProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240392(402-406)Online publication date: 27-Sep-2018
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Quadrana MCremonesi PJannach D(2018)Sequence-Aware Recommender SystemsACM Computing Surveys10.1145/319061651:4(1-36)Online publication date: 6-Jul-2018
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Massimo DBerkovsky SHijikata YRekimoto JBurnett MBillinghurst MQuigley A(2018)User Preference Modeling and Exploitation in IoT ScenariosProceedings of the 23rd International Conference on Intelligent User Interfaces10.1145/3172944.3173151(675-676)Online publication date: 5-Mar-2018
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Post sequential patterns mining: a new method for discovering structural patterns

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