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Article

Evaluation of Item-Based Top-N Recommendation Algorithms

Author:

George KarypisAuthors Info & Claims

CIKM '01: Proceedings of the tenth international conference on Information and knowledge management

Pages 247 - 254

https://doi.org/10.1145/502585.502627

Published: 05 October 2001 Publication History

Abstract

The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems---a personalized information filtering technology used to identify a set of N items that will be of interest to a certain user. User-based Collaborative filtering is the most successful technology for building recommender systems to date, and is extensively used in many commercial recommender systems. Unfortunately, the computational complexity of these methods grows linearly with the number of customers that in typical commercial applications can grow to be several millions. To address these scalability concerns item-based recommendation techniques have been developed that analyze the user-item matrix to identify relations between the different items, and use these relations to compute the list of recommendations.In this paper we present one such class of item-based recommendation algorithms that first determine the similarities between the various items and then used them to identify the set of items to be recommended. The key steps in this class of algorithms are (i) the method used to compute the similarity between the items, and (ii) the method used to combine these similarities in order to compute the similarity between a basket of items and a candidate recommender item. Our experimental evaluation on five different datasets show that the proposed item-based algorithms are up to 28 times faster than the traditional user-neighborhood based recommender systems and provide recommendations whose quality is up to 27% better.

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Cited By

Chee JKalyanaraman SErnala SWeinsberg UDean SIoannidis SBaeza-Yates RBonchi F(2024)Harm Mitigation in Recommender Systems under User Preference DynamicsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671925(255-265)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671925
Yan MLiu FSun JSun FCheng ZHan YHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Behavior-Contextualized Item Preference Modeling for Multi-Behavior RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657696(946-955)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657696
Wang CWang HWang JFeng G(2024)AutoSR: Automatic Sequential Recommendation System DesignIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.340003136:11(5647-5660)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3400031
Show More Cited By

Index Terms

Evaluation of Item-Based Top-N Recommendation Algorithms
1. Applied computing
  1. Electronic commerce
    1. Electronic data interchange
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
    2. Users and interactive retrieval
      1. Personalization
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization

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Published In

cover image ACM Conferences

CIKM '01: Proceedings of the tenth international conference on Information and knowledge management

October 2001

616 pages

ISBN:1581134363

DOI:10.1145/502585

Editors:
Henrique Paques
Georgia Institute of Technology
,
Ling Liu
Georgia Institute of Technology
,
David Grossman
Illinois Institute of Technology
,
General Chair:
Calton Pu
Georgia Institute of Technology

Copyright © 2001 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 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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Published: 05 October 2001

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October 5 - 10, 2001

Georgia, Atlanta, USA

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Chee JKalyanaraman SErnala SWeinsberg UDean SIoannidis SBaeza-Yates RBonchi F(2024)Harm Mitigation in Recommender Systems under User Preference DynamicsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671925(255-265)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671925
Yan MLiu FSun JSun FCheng ZHan YHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Behavior-Contextualized Item Preference Modeling for Multi-Behavior RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657696(946-955)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657696
Wang CWang HWang JFeng G(2024)AutoSR: Automatic Sequential Recommendation System DesignIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.340003136:11(5647-5660)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3400031
Li RMa H(2024)Research on the Application of Personalized Recommendation Algorithm Based on Big Data Analysis in Digital Marketing2024 Second International Conference on Data Science and Information System (ICDSIS)10.1109/ICDSIS61070.2024.10594126(1-6)Online publication date: 17-May-2024
https://doi.org/10.1109/ICDSIS61070.2024.10594126
Zhang YBai GGao ZZhu PLi S(2024)Modeling Long- and Short-Term Project Relationships for Project Management SystemsIEEE Access10.1109/ACCESS.2024.340244812(72242-72251)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3402448
Ge YBertino EWang HCao JZhang Y(2023)Distributed Cooperative Coevolution of Data Publishing Privacy and TransparencyACM Transactions on Knowledge Discovery from Data10.1145/361396218:1(1-23)Online publication date: 6-Sep-2023
https://dl.acm.org/doi/10.1145/3613962
Sun YHan YFan J(2023)Laplacian-based Cluster-Contractive t-SNE for High-Dimensional Data VisualizationACM Transactions on Knowledge Discovery from Data10.1145/361293218:1(1-22)Online publication date: 6-Sep-2023
https://dl.acm.org/doi/10.1145/3612932
Chen KLiu LJiang LChen J(2023)Self-Supervised Dynamic Graph Representation Learning via Temporal Subgraph ContrastACM Transactions on Knowledge Discovery from Data10.1145/361293118:1(1-20)Online publication date: 6-Sep-2023
https://dl.acm.org/doi/10.1145/3612931
Liu BLi LXiao YWang KHu JLiu JChen QHuang R(2023)An Efficient Transfer Learning Method with Auxiliary InformationACM Transactions on Knowledge Discovery from Data10.1145/361293018:1(1-23)Online publication date: 6-Sep-2023
https://dl.acm.org/doi/10.1145/3612930
Luo XWu DGu YChen CLiu LMa JZhang MDeng MHuang JHua X(2023)Criterion-based Heterogeneous Collaborative Filtering for Multi-behavior Implicit RecommendationACM Transactions on Knowledge Discovery from Data10.1145/361131018:1(1-26)Online publication date: 6-Sep-2023
https://dl.acm.org/doi/10.1145/3611310
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