research-article

On Scalability of Association-rule-based Recommendation: A Unified Distributed-computing Framework

Authors:

Yong GeAuthors Info & Claims

ACM Transactions on the Web (TWEB), Volume 14, Issue 3

Article No.: 13, Pages 1 - 21

https://doi.org/10.1145/3398202

Published: 21 June 2020 Publication History

Abstract

The association-rule-based approach is one of the most common technologies for building recommender systems and it has been extensively adopted for commercial use. A variety of techniques, mainly including eligible rule selection and multiple rules combination, have been developed to create effective recommendation. Unfortunately, little attention has been paid to the scalability concern of rule-based recommendation methods. However, the computational complexity of rule-based methods shall increase drastically with the growth of both online customers and rules, which are usually several millions in typical e-commerce platforms. Moreover, the dynamic change of users’ actions requires rule-based methods make recommendations in nearly real-time, which further highlights the scalability issue of rule-based recommender systems. In this article, we present a distributed framework that can scale different association-rule-based recommendation methods in a unified way. Specifically, based on the summarization of existing rule-based approaches, a generic tree-type structure is defined to store separate kinds of patterns, and an efficient algorithm is designed for mining eligible patterns along with computing recommendation scores. To handle the ever-increasing number of online customers, a distributed framework is proposed, where two load-balanced strategies for partitioning tree are put forward to fit sparse and dense data, respectively. Extensive experiments on five real-life data sets demonstrate that the efficiency of association-rule-based recommender systems can be significantly improved by the proposed framework.

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Index Terms

On Scalability of Association-rule-based Recommendation: A Unified Distributed-computing Framework
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
2. Information systems
  1. Information systems applications
    1. Data mining
      1. Association rules
  2. World Wide Web
    1. Web applications
      1. Electronic commerce
    2. Web searching and information discovery
      1. Personalization

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cover image ACM Transactions on the Web

ACM Transactions on the Web Volume 14, Issue 3

August 2020

126 pages

ISSN:1559-1131

EISSN:1559-114X

DOI:10.1145/3398019

Editor:
Brian D. Davison
Lehigh University, USA

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Copyright © 2020 ACM.

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Publication History

Published: 21 June 2020

Online AM: 07 May 2020

Accepted: 01 May 2020

Revised: 01 April 2019

Received: 01 December 2017

Published in TWEB Volume 14, Issue 3

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Industry Projects in Jiangsu S8T Pillar Program
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