More Web Proxy on the site http://driver.im/

research-article

Semantic news recommendation using wordnet and bing similarities

Authors:

Michel Capelle,

Frederik Hogenboom,

Alexander Hogenboom,

Flavius FrasincarAuthors Info & Claims

SAC '13: Proceedings of the 28th Annual ACM Symposium on Applied Computing

Pages 296 - 302

https://doi.org/10.1145/2480362.2480426

Published: 18 March 2013 Publication History

Abstract

While traditionally content-based news recommendation was performed using the word vector space model, more recent approaches also take into account semantics, often through the use of semantic lexicons. However, named entities are rarely taken into account, as they are often absent in such lexicons. Nevertheless, they can play a crucial role in determining user interest for specific news articles. Therefore, in this work, we extend the state-of-the-art semantic lexicon-driven Semantic Similarity (SS) recommendation method by additionally considering named entities. First, as in SS, we calculate similarities between WordNet synonym sets in unread news items and synonym sets in read news items (stored in user profiles). Then, we use the page counts of named entities that are retrieved from the Bing Web search engine to compute named entity similarities between unread and read news items. Results show that our recommendation method, BingSS, outperforms SS in terms of F₁, precision, accuracy, and specificity.

References

[1]

G. Adomavicius and A. Tuzhilin. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6):734--749, 2005.

Digital Library

[2]

Alias-i. LingPipe 4.1.0. http://alias-i.com/lingpipe, 2008.

[3]

S. Banerjee and T. Pedersen. An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet. In A. F. Gelbukh, editor, 4th International Conference on Computational Linguistics and Intelligent Text Processing (CICLING 2002), pages 136--145. Springer-Verlag, 2002.

Digital Library

[4]

Bing. Bing API 2.0. http://www.bing.com/developers/s/APIBasics.html, 2012.

[5]

D. Bollegala, Y. Matsuo, and M. Ishizuka. Measuring Semantic Similarity between Words Using Web Search Engines. In C. L. Williamson, M. E. Zurko, P. F. Patel-Schneider, and P. J. Shenoy, editors, 16th International Conference on World Wide Web (WWW 2007), pages 757--766. ACM, 2007.

Digital Library

[6]

G. Bouma. Normalized (Pointwise) Mutual Information in Collocation Extraction. In C. Chiarcos, R. E. de Castilho, and M. Stede, editors, Biennial GSCL Conference 2009 (GSCL 2009), pages 31--40. Gunter Narr Verlag Tübingen, 2009.

[7]

M. Capelle, M. Moerland, F. Frasincar, and F. Hogenboom. Semantics-Based News Recommendation. In R. Akerkar, C. Bădică, and D. Dan Burdescu, editors, 2nd International Conference on Web Intelligence, Mining and Semantics (WIMS 2012). ACM, 2012.

Digital Library

[8]

R. Cilibrasi and P. M. B. Vitányi. Similarity of Objects and the Meaning of Words. In J. yi Cai, S. B. Cooper, and A. Li, editors, 3rd International Conference on Theory and Applications of Models of Computation (TAMC 2006), volume 3959 of Lecture Notes in Computer Science, pages 21--45, 2006.

Digital Library

[9]

R. Cilibrasi and P. M. B. Vitányi. The Google Similarity Distance. IEEE Transactions on Knowledge and Data Engineering, 19(3):370--383, 2007.

Digital Library

[10]

H. Cunningham, D. Maynard, K. Bontcheva, and V. Tablan. GATE: A Framework and Graphical Development Environment for Robust NLP Tools and Applications. In 40th Anniversary Meeting of the Association for Computational Linguistics (ACL 2002), pages 168--175. Association for Computational Linguistics, 2002.

Digital Library

[11]

C. Fellbaum. WordNet: An Electronic Lexical Database. MIT Press, 1998.

[12]

F. Frasincar, J. Borsje, and L. Levering. A Semantic Web-Based Approach for Building Personalized News Services. International Journal of E-Business Research, 5(3):35--53, 2009.

[13]

F. Goossen, W. IJntema, F. Frasincar, F. Hogenboom, and U. Kaymak. News Personalization using the CF-IDF Semantic Recommender. In R. Akerkar, editor, International Conference on Web Intelligence, Mining and Semantics (WIMS 2011). ACM, 2011.

Digital Library

[14]

I. R. Group. WordWideWebSize.com. http://www.worldwidewebsize.com, 2012.

[15]

D. Hope. Sussex University: NLP Lab, Homepage. http://www.cogs.susx.ac.uk/users/drh21/, 2012.

[16]

W. IJntema, F. Goossen, F. Frasincar, and F. Hogenboom. Ontology-Based News Recommendation. In F. Daniel, L. M. L. Delcambre, F. Fotouhi, I. Garrigós, G. Guerrini, J.-N. Mazón, M. Mesiti, S. Müller-Feuerstein, J. Trujillo, T. M. Truta, B. Volz, E. Waller, L. Xiong, and E. Zimányi, editors, International Workshop on Business intelligencE and the WEB (BEWEB 2010) at Thirteenth International Conference on Extending Database Technology and Thirteenth International Conference on Database Theory (EDBT/ICDT 2010). ACM, 2010.

Digital Library

[17]

P. Jaccard. Étude Comparative de la Distribution Florale dans une Portion des Alpes et des Jura. Bulletin de la Société Vaudoise des Sciences Naturelles, 37:547--579, 1901.

[18]

A. S. Jensen and N. S. Boss. Textual Similarity: Comparing Texts in Order to Discover How Closely They Discuss the Same Topics. Bachelor's Thesis, Technical University of Denmark, 2008.

[19]

J. J. Jiang and D. W. Conrath. Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. In 10th International Conference on Research in Computational Linguistics (ROCLING 1997), pages 19--33, 1997.

[20]

C. Leacock and M. Chodorow. WordNet: An Electronic Lexical Database, chapter Combining Local Context and WordNet Similarity for Word Sense Identification, pages 265--283. MIT Press, 1998.

[21]

Lextek. Onix Text Retrieval Toolkit -- API Reference. http://www.lextek.com/manuals/onix/stopwords1.html, 2012.

[22]

D. Lin. An Information-Theoretic Definition of Similarity. In J. W. Shavlik, editor, 15th International Conference on Machine Learning (ICML 1998), pages 296--304. Morgan Kaufmann, 1998.

Digital Library

[23]

T. Pedersen, S. Patwardhan, and J. Michelizzi. WordNet::Similarity -- Measuring the Relatedness of Concepts. In D. L. McGuinness and G. Ferguson, editors, 19th National Conference on Artificial Intelligence (AAAI 2004), pages 1024--1025. AAAI Press/MIT Press, 2004.

Digital Library

[24]

P. Resnik. Using Information Content to Evaluate Semantic Similarity in a Taxonomy. In 14th International Joint Conference on Artificial Intelligence (IJCAI 1995), pages 448--453. Morgan Kaufmann, 1995.

Digital Library

[25]

G. Salton and C. Buckley. Term-Weighting Approaches in Automatic Text Retrieval. Information Processing and Management, 24(5):513--523, 1988.

Digital Library

[26]

J. Schafer, J. Konstan, and J. Riedi. Recommender Systems in E-Commerce. In 1st ACM Conference on Electronic Commerce (ACM-EC 1999), pages 158--166, 1999.

Digital Library

[27]

K. Toutanova, D. Klein, C. D. Manning, and Y. Singer. Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network. In Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLTNAACL 2003), pages 252--259, 2003.

Digital Library

[28]

P. M. B. Vitányi. Universal Similarity. In 2005 IEEE Information Theory Workshop (ITW 2005), pages 6--10, 2005.

[29]

Z. Wu and M. S. Palmer. Verb Semantics and Lexical Selection. In 32nd Annual Meeting of the Association for Computational Linguistics (ACL 1994), pages 133--138. Association for Computational Linguistics, 1994.

Digital Library

[30]

C.-N. Ziegler. Semantic Web Recommender Systems. In W. Lindner, M. Mesiti, C. Türker, Y. Tzitzikas, and A. Vakali, editors, EDBT 2004 Workshops, volume 3268 of Lecture Notes in Computer Science, pages 78--79. Springer, 2004.

Digital Library

Cited By

Saifullah SDreżewski RDwiyanto FAribowo AFauziah YCahyana N(2024)Automated Text Annotation Using a Semi-Supervised Approach with Meta Vectorizer and Machine Learning Algorithms for Hate Speech DetectionApplied Sciences10.3390/app1403107814:3(1078)Online publication date: 26-Jan-2024
https://doi.org/10.3390/app14031078
Iana AAlam MPaulheim H(2024)A survey on knowledge-aware news recommender systemsSemantic Web10.3233/SW-22299115:1(21-82)Online publication date: 12-Jan-2024
https://doi.org/10.3233/SW-222991
Starke ASolberg VØverhaug STrattner C(2024)Examining the merits of feature-specific similarity functions in the news domain using human judgmentsUser Modeling and User-Adapted Interaction10.1007/s11257-024-09412-234:4(995-1042)Online publication date: 7-Aug-2024
https://doi.org/10.1007/s11257-024-09412-2
Show More Cited By

Index Terms

Semantic news recommendation using wordnet and bing similarities
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
2. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

Recommendations

Semantics-based news recommendation
WIMS '12: Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics

News item recommendation is commonly performed using the TF-IDF weighting technique in combination with the cosine similarity measure. However, this technique does not take into account the actual meaning of words. Therefore, we propose two new methods ...
A semantic enhanced hybrid recommendation approach

Recommender systems are effectively used as a personalized information filtering technology to automatically predict and identify a set of interesting items on behalf of users according to their personal needs and preferences. Collaborative Filtering (...
Semantics-based news recommendation with SF-IDF+
WIMS '13: Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics

Content-based news recommendations are usually made by employing the cosine similarity and the TF-IDF weighting scheme for terms occurring in news messages and user profiles. Recent developments, such as SF-IDF, have elevated news recommendation to a ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SAC '13: Proceedings of the 28th Annual ACM Symposium on Applied Computing

March 2013

2124 pages

ISBN:9781450316569

DOI:10.1145/2480362

Conference Chairs:
Sung Y. Shin
South Dakota State University, United States
,
José Carlos Maldonado
ICMC - University of São Paulo, Brazil

Copyright © 2013 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]

Sponsors

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 March 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SAC '13

Sponsor:

SIGAPP

SAC '13: SAC '13

March 18 - 22, 2013

Coimbra, Portugal

Acceptance Rates

SAC '13 Paper Acceptance Rate 255 of 1,063 submissions, 24%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Upcoming Conference

SAC '25

Sponsor:
sigapp

The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

21
Total Citations
View Citations
293
Total Downloads

Downloads (Last 12 months)8
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Saifullah SDreżewski RDwiyanto FAribowo AFauziah YCahyana N(2024)Automated Text Annotation Using a Semi-Supervised Approach with Meta Vectorizer and Machine Learning Algorithms for Hate Speech DetectionApplied Sciences10.3390/app1403107814:3(1078)Online publication date: 26-Jan-2024
https://doi.org/10.3390/app14031078
Iana AAlam MPaulheim H(2024)A survey on knowledge-aware news recommender systemsSemantic Web10.3233/SW-22299115:1(21-82)Online publication date: 12-Jan-2024
https://doi.org/10.3233/SW-222991
Starke ASolberg VØverhaug STrattner C(2024)Examining the merits of feature-specific similarity functions in the news domain using human judgmentsUser Modeling and User-Adapted Interaction10.1007/s11257-024-09412-234:4(995-1042)Online publication date: 7-Aug-2024
https://doi.org/10.1007/s11257-024-09412-2
Wu CWu FHuang YXie X(2023)Personalized News Recommendation: Methods and ChallengesACM Transactions on Information Systems10.1145/353025741:1(1-50)Online publication date: 10-Jan-2023
https://dl.acm.org/doi/10.1145/3530257
Wang JTan JChen JJin H(2021)A Knowledge-aware and Time-sensitive Financial News Recommendation System Based on Firm Relation Derivation2021 International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW53433.2021.00146(1104-1111)Online publication date: Dec-2021
https://doi.org/10.1109/ICDMW53433.2021.00146
Borovič MFerme MBrezovnik JMajninger SKac KOjsteršek M(2020)Document Recommendations and Feedback Collection Analysis within the Slovenian Open-Access InfrastructureInformation10.3390/info1111049711:11(497)Online publication date: 23-Oct-2020
https://doi.org/10.3390/info11110497
van Huijsduijnen LHoogmoed TKeulers GLangendoen ELangendoen SVos THogenboom FFrasincar FRobal T(2020)Bing-CSF-IDF+: A Semantics-Driven Recommender System for NewsNew Trends in Databases and Information Systems10.1007/978-3-030-54623-6_13(143-153)Online publication date: 17-Aug-2020
https://doi.org/10.1007/978-3-030-54623-6_13
Afolabi IMakinde OOladipupo O(2019)Semantic Web mining for Content-Based Online Shopping Recommender SystemsInternational Journal of Intelligent Information Technologies10.4018/IJIIT.201910010315:4(41-56)Online publication date: Oct-2019
https://doi.org/10.4018/IJIIT.2019100103
Dessì DReforgiato Recupero DFenu GConsoli S(2018)A Recommender System of Medical Reports Leveraging Cognitive Computing and Frame SemanticsMachine Learning Paradigms10.1007/978-3-319-94030-4_2(7-30)Online publication date: 4-Jul-2018
https://doi.org/10.1007/978-3-319-94030-4_2
de Koning EHogenboom FFrasincar F(2018)News Recommendation with CF-IDF+Advanced Information Systems Engineering10.1007/978-3-319-91563-0_11(170-184)Online publication date: 17-May-2018
https://doi.org/10.1007/978-3-319-91563-0_11
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents