Article

Latent semantic analysis for multiple-type interrelated data objects

Authors:

ChengXiang ZhaiAuthors Info & Claims

SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 236 - 243

https://doi.org/10.1145/1148170.1148214

Published: 06 August 2006 Publication History

Abstract

Co-occurrence data is quite common in many real applications. Latent Semantic Analysis (LSA) has been successfully used to identify semantic relations in such data. However, LSA can only handle a single co-occurrence relationship between two types of objects. In practical applications, there are many cases where multiple types of objects exist and any pair of these objects could have a pairwise co-occurrence relation. All these co-occurrence relations can be exploited to alleviate data sparseness or to represent objects more meaningfully. In this paper, we propose a novel algorithm, M-LSA, which conducts latent semantic analysis by incorporating all pairwise co-occurrences among multiple types of objects. Based on the mutual reinforcement principle, M-LSA identifies the most salient concepts among the co-occurrence data and represents all the objects in a unified semantic space. M-LSA is general and we show that several variants of LSA are special cases of our algorithm. Experiment results show that M-LSA outperforms LSA on multiple applications, including collaborative filtering, text clustering, and text categorization.

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Naili MChaibi AGhezala H(2018)The Contribution of Stemming and Semantics in Arabic Topic SegmentationACM Transactions on Asian and Low-Resource Language Information Processing10.1145/315246417:2(1-25)Online publication date: 11-Jan-2018
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Zhang XZhang XLi XLi ZWang S(2018)Classify social image by integrating multi-modal contentMultimedia Tools and Applications10.1007/s11042-017-4657-277:6(7469-7485)Online publication date: 1-Mar-2018
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Index Terms

Latent semantic analysis for multiple-type interrelated data objects
1. Information systems
  1. Information retrieval
    1. Document representation
    2. Search engine architectures and scalability
      1. Search engine indexing

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Published In

cover image ACM Conferences

SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval

August 2006

768 pages

ISBN:1595933697

DOI:10.1145/1148170

General Chair:
Efthimis N. Efthimiadis
University of Washington
,
Program Chairs:
Susan Dumais
Microsoft Research, Redmond
,
David Hawking
CSIRO ICT Centre, Canberra, Australia
,
Kalervo Järvelin,
University of Tampere, Finland

Copyright © 2006 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: 06 August 2006

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SIGIR06: The 29th Annual International SIGIR Conference

August 6 - 11, 2006

Washington, Seattle, USA

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Naili MChaibi AGhezala H(2018)The Contribution of Stemming and Semantics in Arabic Topic SegmentationACM Transactions on Asian and Low-Resource Language Information Processing10.1145/315246417:2(1-25)Online publication date: 11-Jan-2018
https://dl.acm.org/doi/10.1145/3152464
Zhang XZhang XLi XLi ZWang S(2018)Classify social image by integrating multi-modal contentMultimedia Tools and Applications10.1007/s11042-017-4657-277:6(7469-7485)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s11042-017-4657-2
Aleroud AGangopadhyay A(2018)Multimode co-clustering for analyzing terrorist networksInformation Systems Frontiers10.1007/s10796-016-9712-420:5(1053-1074)Online publication date: 1-Oct-2018
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Wei S(2018)Research on the Gene Regulation Network Construction AlgorithmInternational Conference on Applications and Techniques in Cyber Security and Intelligence ATCI 201810.1007/978-3-319-98776-7_135(1107-1112)Online publication date: 5-Nov-2018
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Liu CZhang XLi XLi RZhang XChao W(2016)Multi-modal learning for social image classification2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)10.1109/FSKD.2016.7603345(1174-1179)Online publication date: Aug-2016
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Gan MSun LJiang R(2016)Trinity: Walking on a User-Object-Tag Heterogeneous Network for Personalised RecommendationsJournal of Computer Science and Technology10.1007/s11390-016-1648-031:3(577-594)Online publication date: 6-May-2016
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Zhang XLi ZLv XChen X(2016)Integrating multiple types of features for event identification in social imagesMultimedia Tools and Applications10.1007/s11042-014-2436-x75:6(3301-3322)Online publication date: 1-Mar-2016
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Du YYao CLi N(2015)Using heterogeneous patent network features to rank and discover influential inventorsFrontiers of Information Technology & Electronic Engineering10.1631/FITEE.140039416:7(568-578)Online publication date: 12-Jul-2015
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Shi YLarson MHanjalic A(2014)Collaborative Filtering beyond the User-Item MatrixACM Computing Surveys10.1145/255627047:1(1-45)Online publication date: 1-May-2014
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