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Collaborative filtering based on an iterative prediction method to alleviate the sparsity problem

Authors:

Amira Abdelwahab,

Yasuo Horiuchi,

Shingo KuroiwaAuthors Info & Claims

iiWAS '09: Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services

Pages 375 - 379

https://doi.org/10.1145/1806338.1806406

Published: 14 December 2009 Publication History

Abstract

Collaborative filtering (CF) is one of the most popular recommender system technologies. It tries to identify users that have relevant interests and preferences by calculating similarities among user profiles. The idea behind this method is that, it may be of benefit to one's search for information to consult the preferences of other users who share the same or relevant interests and whose opinion can be trusted. However, the applicability of CF is limited due to the sparsity and cold-start problems. The sparsity problem occurs when available data are insufficient for identifying similar users (neighbors) and it is a major issue that limits the quality of recommendations and the applicability of CF in general. Additionally, the cold-start problem occurs when dealing with new users and new or updated items in web environments. Therefore, we propose an efficient iterative prediction technique to convert user-item sparse matrix to dense one and overcome the cold-start problem. Our experiments with MovieLens and book-crossing data sets indicate substantial and consistent improvements in recommendations accuracy compared with item-based collaborative filtering, singular value decomposition (SVD)-based collaborative filtering and semi explicit rating collaborative filtering.

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Cited By

Iftikhar AGhazanfar MAyub MMehmood ZMaqsood M(2020)An Improved Product Recommendation Method for Collaborative FilteringIEEE Access10.1109/ACCESS.2020.30059538(123841-123857)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3005953
AL-Bakri NHassan S(2019)A Proposed Model to Solve Cold Start Problem using Fuzzy User-Based Clustering2019 2nd Scientific Conference of Computer Sciences (SCCS)10.1109/SCCS.2019.8852624(121-125)Online publication date: Mar-2019
https://doi.org/10.1109/SCCS.2019.8852624
Zhou ZWang BGuo JPan J(2015)QoS-Aware Web Service Recommendation Using Collaborative Filtering with PGraphProceedings of the 2015 IEEE International Conference on Web Services10.1109/ICWS.2015.59(392-399)Online publication date: 27-Jun-2015
https://dl.acm.org/doi/10.1109/ICWS.2015.59
Show More Cited By

Index Terms

Collaborative filtering based on an iterative prediction method to alleviate the sparsity problem
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
  2. Information systems applications
    1. Data mining
      1. Clustering

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cover image ACM Other conferences

iiWAS '09: Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services

December 2009

763 pages

ISBN:9781605586601

DOI:10.1145/1806338

General Chair:
Gabriele Kotsis
Johannes Kepler University Linz, Austria
,
Program Chairs:
David Taniar
Monash University, Australia
,
Eric Pardede
La Trobe University, Australia

Copyright © 2009 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 December 2009

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iiWAS '09

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iiWAS '09: 11th International Conference on Information Integration and Web-based Applications & Services

December 14 - 16, 2009

Kuala Lumpur, Malaysia

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Cited By

Iftikhar AGhazanfar MAyub MMehmood ZMaqsood M(2020)An Improved Product Recommendation Method for Collaborative FilteringIEEE Access10.1109/ACCESS.2020.30059538(123841-123857)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3005953
AL-Bakri NHassan S(2019)A Proposed Model to Solve Cold Start Problem using Fuzzy User-Based Clustering2019 2nd Scientific Conference of Computer Sciences (SCCS)10.1109/SCCS.2019.8852624(121-125)Online publication date: Mar-2019
https://doi.org/10.1109/SCCS.2019.8852624
Zhou ZWang BGuo JPan J(2015)QoS-Aware Web Service Recommendation Using Collaborative Filtering with PGraphProceedings of the 2015 IEEE International Conference on Web Services10.1109/ICWS.2015.59(392-399)Online publication date: 27-Jun-2015
https://dl.acm.org/doi/10.1109/ICWS.2015.59
Liu HHu ZMian ATian HZhu X(2014)A new user similarity model to improve the accuracy of collaborative filteringKnowledge-Based Systems10.1016/j.knosys.2013.11.00656(156-166)Online publication date: Jan-2014
https://doi.org/10.1016/j.knosys.2013.11.006
Zheng XDing WXu JChen D(2014)Personalized recommendation based on review topicsService Oriented Computing and Applications10.1007/s11761-013-0140-88:1(15-31)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1007/s11761-013-0140-8
Xu JZheng XDing W(2012)Personalized Recommendation Based on Reviews and Ratings Alleviating the Sparsity Problem of Collaborative FilteringProceedings of the 2012 IEEE Ninth International Conference on e-Business Engineering10.1109/ICEBE.2012.12(9-16)Online publication date: 9-Sep-2012
https://dl.acm.org/doi/10.1109/ICEBE.2012.12
Bilge APolat H(2011)An Improved Profile-Based CF Scheme with PrivacyProceedings of the 2011 IEEE Fifth International Conference on Semantic Computing10.1109/ICSC.2011.20(133-140)Online publication date: 18-Sep-2011
https://dl.acm.org/doi/10.1109/ICSC.2011.20

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