extended-abstract

Behavioral Patterns Mining for Online Time Personalization

Authors:

Tomas Chovanak,

Ondrej Kassak,

Maria BielikovaAuthors Info & Claims

UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

Pages 361 - 362

https://doi.org/10.1145/3079628.3079631

Published: 09 July 2017 Publication History

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Abstract

Behavioral patterns represent repeating sequences or sets of actions, which website users often perform together. Such patterns can be used to identify user preferences, recommend interesting content to him, etc. For dynamic sites with fast changing content (e.g., news, social networks) we need to recognize such patterns in an online time. In this paper, we introduce a novel method for recognizing behavioral patterns in an online time over a data stream. Main contribution is a combination of global patterns with patterns specific for groups of similar users. We evaluated the method using a personalized recommendation task over datasets from news and e-learning domains and show that the combination of common global and specific group patterns reaches higher recommendation precision than its components used individually.

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Cited By

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Jansen BJung SRobillos DSalminen J(2021)Next Likely Behavior: Predicting Individual Actions from Aggregate User Behaviors2021 Second International Conference on Intelligent Data Science Technologies and Applications (IDSTA)10.1109/IDSTA53674.2021.9660806(11-15)Online publication date: 15-Nov-2021
https://doi.org/10.1109/IDSTA53674.2021.9660806

Index Terms

Behavioral Patterns Mining for Online Time Personalization
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
2. Information systems
  1. Information systems applications
    1. Data mining
      1. Data stream mining
  2. World Wide Web
    1. Web mining
      1. Web log analysis

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Information

Published In

UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

July 2017

420 pages

ISBN:9781450346351

DOI:10.1145/3079628

General Chairs:
Maria Bielikova
Slovak University of Technology in Bratislava, Slovakia
,
Eelco Herder
L3S Research Center, Germany and Radboud Universiteit Nijmegen, The Netherlands
,
Program Chairs:
Federica Cena
University of Torino, Italy
,
Michel Desmarais
Polytechnique University, Montreal, Canada

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 09 July 2017

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Slovak Research and Development Agency
Scientific Grant Agency of the Slovak Republic

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UMAP '17

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UMAP '17: 25th Conference on User Modeling, Adaptation and Personalization

July 9 - 12, 2017

Bratislava, Slovakia

Acceptance Rates

UMAP '17 Paper Acceptance Rate 29 of 80 submissions, 36%;

Overall Acceptance Rate 162 of 633 submissions, 26%

Upcoming Conference

UMAP '25

Sponsor:
sigchi
sigchi

33rd ACM Conference on User Modeling, Adaptation and Personalization

June 16 - 19, 2025

New York City , NY , USA

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Jansen BJung SRobillos DSalminen J(2021)Next Likely Behavior: Predicting Individual Actions from Aggregate User Behaviors2021 Second International Conference on Intelligent Data Science Technologies and Applications (IDSTA)10.1109/IDSTA53674.2021.9660806(11-15)Online publication date: 15-Nov-2021
https://doi.org/10.1109/IDSTA53674.2021.9660806

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Recommendations

Fast Streaming Behavioural Pattern Mining

Time-weighted counting for recently frequent pattern mining in data streams

Personalized recommendation of learning material using sequential pattern mining and attribute based collaborative filtering

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