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Collaborative filtering and the generalized vector space model (poster session)

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SIGIR '00: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval

Pages 351 - 353

https://doi.org/10.1145/345508.345646

Published: 01 July 2000 Publication History

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Abstract

Collaborative filtering is a technique for recommending documents to users based on how similar their tastes are to other users. If two users tend to agree on what they like, the system will recommend the same documents to them. The generalized vector space model of information retrieval represents a document by a vector of its similarities to all other documents. The process of collaborative filtering is nearly identical to the process of retrieval using GVSM in a matrix of user ratings. Using this observation, a model for filtering collaboratively using document content is possible.

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Cited By

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Gironacci I(2021)Literature Review of Recommendation SystemsTransdisciplinary Perspectives on Risk Management and Cyber Intelligence10.4018/978-1-7998-4339-9.ch009(119-129)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-4339-9.ch009
Zamani HShakery A(2018)A language model-based framework for multi-publisher content-based recommender systemsInformation Retrieval Journal10.1007/s10791-018-9327-021:5(369-409)Online publication date: 6-Feb-2018
https://doi.org/10.1007/s10791-018-9327-0
Guerraoui RKermarrec ALin TPatra R(2017)Heterogeneous recommendationsProceedings of the VLDB Endowment10.14778/3115404.311541210:10(1070-1081)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.14778/3115404.3115412
Show More Cited By

Index Terms

Collaborative filtering and the generalized vector space model (poster session)
1. 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

SIGIR '00: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval

July 2000

396 pages

ISBN:1581132263

DOI:10.1145/345508

Chairmen:
Emmanuel Yannakoudakis
Athens Univ. of Economics and Business, Greece
,
Nicholas J. Belkin
Rutgers Univ.
,
Mun-Kew Leong
Kent Ridge Digital Labs
,
Peter Ingwersen
Royal School of Library and Information Science

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: 01 July 2000

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Greek Com Soc
SIGIR
Athens U of Econ & Business

SIGIR00: 23rd ACM International SIGIR Conference on Research and Development in Information Retrieval

July 24 - 28, 2000

Athens, Greece

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Cited By

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Gironacci I(2021)Literature Review of Recommendation SystemsTransdisciplinary Perspectives on Risk Management and Cyber Intelligence10.4018/978-1-7998-4339-9.ch009(119-129)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-4339-9.ch009
Zamani HShakery A(2018)A language model-based framework for multi-publisher content-based recommender systemsInformation Retrieval Journal10.1007/s10791-018-9327-021:5(369-409)Online publication date: 6-Feb-2018
https://doi.org/10.1007/s10791-018-9327-0
Guerraoui RKermarrec ALin TPatra R(2017)Heterogeneous recommendationsProceedings of the VLDB Endowment10.14778/3115404.311541210:10(1070-1081)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.14778/3115404.3115412
Xu BZhuge H(2016)An angle-based interest model for text recommendationFuture Generation Computer Systems10.1016/j.future.2016.04.01164:C(211-226)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1016/j.future.2016.04.011
Huang JZhu KZhong N(2016)A probabilistic inference model for recommender systemsApplied Intelligence10.1007/s10489-016-0783-145:3(686-694)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1007/s10489-016-0783-1
Wibowo ARahmawati A(2015)Naïve Random Neighbor Selection for memory based Collaborative Filtering2015 International Seminar on Intelligent Technology and Its Applications (ISITIA)10.1109/ISITIA.2015.7220005(351-356)Online publication date: May-2015
https://doi.org/10.1109/ISITIA.2015.7220005
Wang SSun JGao BMa J(2014)VSRankACM Transactions on Intelligent Systems and Technology10.1145/25420485:3(1-24)Online publication date: 17-Jul-2014
https://dl.acm.org/doi/10.1145/2542048
Miguel JCaballé SXhafa FPrieto JBarolli L(2014)Predicting Trustworthiness Behavior to Enhance Security in On-line AssessmentProceedings of the 2014 International Conference on Intelligent Networking and Collaborative Systems10.1109/INCoS.2014.19(342-349)Online publication date: 10-Sep-2014
https://dl.acm.org/doi/10.1109/INCoS.2014.19
Chen CWan YChung MSun Y(2013)An effective recommendation method for cold start new users using trust and distrust networksInformation Sciences: an International Journal10.1016/j.ins.2012.10.037224(19-36)Online publication date: 1-Mar-2013
https://dl.acm.org/doi/10.1016/j.ins.2012.10.037
Bellogín AWang JCastells P(2012)Bridging memory-based collaborative filtering and text retrievalInformation Retrieval10.1007/s10791-012-9214-z16:6(697-724)Online publication date: 17-Nov-2012
https://doi.org/10.1007/s10791-012-9214-z
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