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Assessing the Significance of Incorporating User Profiles in Social Book Search

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
Botlhokang Apadile, Edwin Thuma, Gontlafetse Mosweunyane
10.5120/ijca2018916459

Botlhokang Apadile, Edwin Thuma and Gontlafetse Mosweunyane. Assessing the Significance of Incorporating User Profiles in Social Book Search. International Journal of Computer Applications 179(23):1-8, February 2018. BibTeX

@article{10.5120/ijca2018916459,
	author = {Botlhokang Apadile and Edwin Thuma and Gontlafetse Mosweunyane},
	title = {Assessing the Significance of Incorporating User Profiles in Social Book Search},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2018},
	volume = {179},
	number = {23},
	month = {Feb},
	year = {2018},
	issn = {0975-8887},
	pages = {1-8},
	numpages = {8},
	url = {http://www.ijcaonline.org/archives/volume179/number23/29005-2018916459},
	doi = {10.5120/ijca2018916459},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

In this article, it is hypothesized that personalizing the book search application by incorporating user profiles such as background of personal tastes, interests and previously seen books. can issue or produce a more effective query result set as well as an effective book recommendation. To meet this end, experiments were carried out to explore which topic representation gives the best result. Four different query representations, which are title, request, group and a combination of title-request-group were used. It was observed that the title-request-group query representation was best. In addition, an investigation was conducted to determine whether a learning to rank framework that incorporates topical relevance by exploiting user profiles for document re-ranking according to individual preference will issue a more effective result set. Moreover, an investigation was conducted to determine whether the use of keywords from profiles for query expansion and reformulation improves the search results. The results of these investigations suggest that a more effective query result set as well as an effective book recommendation can be attained by incorporating user profiles such as background of personal tastes, interests and previously seen books into the social book search application.

References

  1. Arezki R., Poncelet P., Dray G., and Pearson D.W. Information Retrieval Model Based on User Profile, pages 490–499. Springer Berlin Heidelberg, Berlin, Heidelberg, 2004.
  2. Shardanand U. and Maes P. Social information filtering: Algorithms for automating “word of mouth”. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’95, pages 210–217, New York, NY, USA, 1995. ACM Press/Addison-Wesley Publishing Co.
  3. Koolen M., Kazai G., Kamps J., Preminger M., Doucet A., and Landoni M. Overview of the INEX 2012 social book search track. In CLEF 2012 Evaluation Labs and Workshop, Online Working Notes, Rome, Italy, September 17-20, 2012, 2012.
  4. Koolen M., Bogers T., G¨ade M., Hall M.M., Huurdeman H.C., Kamps J., Skov M., Toms E., and Walsh D. Overview of the CLEF 2015 social book search lab. In Experimental IR Meets Multilinguality, Multimodality, and Interaction - 6th International Conference of the CLEF Association, CLEF 2015, Toulouse, France, September 8-11, 2015, Proceedings, pages 545–564, 2015.
  5. Shokouhi M., Sloan M., Bennett P.N., Collins-Thompson K., and Sarkizova S. Query suggestion and data fusion in contextual disambiguation. In Proceedings of the 24th International Conference on World Wide Web, WWW ’15, pages 971–980, Republic and Canton of Geneva, Switzerland, 2015.
  6. Adomavicius G. and Tuzhilin A. User profiling in personalization applications through rule discovery and validation. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’99, pages 377–381, New York, NY, USA, 1999. ACM.
  7. Gauch S., Speretta M., Chandramouli A., and Micarelli A. User Profiles for Personalized Information Access, pages 54– 89. Springer Berlin Heidelberg, Berlin, Heidelberg, 2007.
  8. Krulwich B. LIFESTYLE FINDER: intelligent user profiling using large-scale demographic data. AI Magazine, 18(2):37– 45, 1997.
  9. Middleton S.E., Shadbolt N.R., and De Roure D.C. Ontological user profiling in recommender systems. ACM Trans. Inf. Syst., 22(1):54–88, January 2004.
  10. Koolen M., Kazai G., Kamps J., Doucet A., and Landoni M. Overview of the INEX 2011 books and social search track. In Focused Retrieval of Content and Structure, 10th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2011, Saarbr¨ucken, Germany, December 12- 14, 2011, Revised Selected Papers, pages 1–29, 2011.
  11. Koolen M., Kazai G., Preminger M., and Doucet A. Overview of the INEX 2013 social book search track. In Working Notes for CLEF 2013 Conference , Valencia, Spain, September 23- 26, 2013., 2013.
  12. Koolen M., Bogers T., Kamps J., Kazai G., and Preminger M. Overview of the INEX 2014 social book search track. In Working Notes for CLEF 2014 Conference, Sheffield, UK, September 15-18, 2014., pages 462–479, 2014.
  13. Zhang B.-W., Yin X.-C., Cui X.-P., Qu J., Geng B., Zhou F., and Hao H.-W. USTB at INEX2014: social book search track. In Working Notes for CLEF 2014 Conference, Sheffield, UK, September 15-18, 2014., pages 536–542, 2014.
  14. Chaa M. and Nouali O. CERIST at INEX 2015: Social book search track. In Working Notes of CLEF 2015 - Conference and Labs of the Evaluation forum, Toulouse, France, September 8-11, 2015., 2015.
  15. Harman D. Is the Cranfield Paradigm Outdated? In Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1–1, New York, NY, USA, 2010. ACM.
  16. Sanderson M. Test Collection Based Evaluation of Information Retrieval Systems. Foundations and Trends in Information Retrieval, 4(4):247–375, June 2010.
  17. Voorhees E.M. The Philosophy of Information Retrieval Evaluation. In Revised Papers from the Second Workshop of the Cross-Language Evaluation Forum on Evaluation of Cross- Language Information Retrieval Systems, pages 355–370, London, UK, UK, 2002. Springer-Verlag.
  18. Saravanan M., Raj P.C.R., and Raman S. Summarization and categorization of text data in high-level data cleaning for information retrieval. Applied Artificial Intelligence, 17(5- 6):461–474, 2003.
  19. Ounis I., Amati G., Plachouras V., He B., Macdonald C., and Johnson. Terrier Information Retrieval Platform. In Proceedings of the 27th European Conference on IR Research, volume 3408 of Lecture Notes in Computer Science, pages 517–519, Berlin, Heidelberg, 2005. Springer-Verlag.
  20. Liu T.-Y. Learning to Rank for Information Retrieval. Foundations and Trends in Information Retrieval, 3(3):225–331, June 2009.
  21. Macdonald C., Santos R.L.T., Ounis I., and He B. About learning models with multiple query-dependent features. ACM Transactions on Information Systems (TOIS), 31(3):11:1–11:39, August 2013.
  22. Macdonald C., Santos R.L.T., and Ounis I. The whens and hows of learning to rank for web search. Information Retrieval, 16(5):584–628, October 2013.
  23. Metzler D. and Croft W.B. Linear feature-based models for information retrieval. Information Retrieval, 10(3):257–274, June 2007.
  24. Burges C.J.C., Ragno R., and Le Q.V. Learning to rank with non-smooth cost functions. In Advances in Neural Information Processing Systems 19. MIT Press, Cambridge, MA, January 2007.

Keywords

Social Book Search, User Profiles