Advertisement

Personalized Social Query Expansion Using Social Annotations

  • Mohamed Reda Bouadjenek
  • Hakim Hacid
  • Mokrane Bouzeghoub
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11360)

Abstract

Query expansion is a query pre-processing technique that adds to a given query, terms that are likely to occur in relevant documents in order to improve information retrieval accuracy. A key problem to solve is “how to identify the terms to be added to a query?” While considering social tagging systems as a data source, we propose an approach that selects terms based on (i) the semantic similarity between tags composing a query, (ii) a social proximity between the query and the user for a personalized expansion, and (iii) a strategy for expanding, on the fly, user queries. We demonstrate the effectiveness of our approach by an intensive evaluation on three large public datasets crawled from delicious, Flickr, and CiteULike. We show that the expanded queries built by our method provide more accurate results as compared to the initial queries, by increasing the MAP in a range of 10 to 16% on the three datasets. We also compare our method to three state of the art baselines, and we show that our query expansion method allows significant improvement in the MAP, with a boost in a range between 5 to 18%.

Keywords

Personalization Social Information Retrieval Social networks Query expansion 

CR Subject Classification:

H.3.3 [Information Systems]: Information Storage and Retrieval Information Search and Retrieval 

Notes

Conflict of Interest

The author(s) declare(s) that there is no conflict of interest regarding the publication of this paper.

References

  1. 1.
    Baeza-Yates, R.A., Ribeiro-Neto, B.: Modern Information Retrieval: The Concepts and Technology Behind Search, 2nd edn. Addison-Wesley Longman Publishing Co. Inc., Boston (2011)Google Scholar
  2. 2.
    Bao, S., Xue, G., Wu, X., Yu, Y., Fei, B., Su, Z.: Optimizing web search using social annotations. In: Proceedings of the 16th International Conference on World Wide Web, WWW 2007, pp. 501–510. ACM, New York (2007)Google Scholar
  3. 3.
    Belkin, N.J.: Some(what) grand challenges for information retrieval. SIGIR Forum 42(1), 47–54 (2008)CrossRefGoogle Scholar
  4. 4.
    Bender, M., et al.: Exploiting social relations for query expansion and result ranking. In: 2008 IEEE 24th International Conference on Data Engineering Workshop (2008)Google Scholar
  5. 5.
    Benz, D., Hotho, A., Jaschke, R., Krause, B., Stumme, G.: Query logs as folksonomies. Datenbank-Spektrum 10, 15–24 (2010)CrossRefGoogle Scholar
  6. 6.
    Bertier, M., Guerraoui, R., Leroy, V., Kermarrec, A.-M.: Toward personalized query expansion. In: Proceedings of the Second ACM EuroSys Workshop on Social Network Systems, SNS 2009, pp. 7–12. ACM, New York (2009)Google Scholar
  7. 7.
    Biancalana, C., Micarelli, A., Squarcella, C.: Nereau: a social approach to query expansion. In: Proceedings of the 10th ACM Workshop on Web Information and Data Management, WIDM 2008, pp. 95–102. ACM, New York (2008)Google Scholar
  8. 8.
    Bischoff, K., Firan, C.S., Nejdl, W., Paiu, R.: Can all tags be used for search? In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM 2008, pp. 193–202. ACM, New York (2008)Google Scholar
  9. 9.
    Bouadjenek, M.R., Hacid, H., Bouzeghoub, M.: LAICOS: an open source platform for personalized social web search. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, pp. 1446–1449. ACM, New York (2013)Google Scholar
  10. 10.
    Bouadjenek, M.R., Hacid, H., Bouzeghoub, M.: SoPRa: a new social personalized ranking function for improving web search. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2013, pp. 861–864. ACM, New York (2013)Google Scholar
  11. 11.
    Bouadjenek, M.R., Hacid, H., Bouzeghoub, M.: Social networks and information retrieval, how are they converging? A survey, a taxonomy and an analysis of social information retrieval approaches and platforms. Inf. Syst. 56, 1–18 (2016)CrossRefGoogle Scholar
  12. 12.
    Bouadjenek, M.R., Hacid, H., Bouzeghoub, M., Daigremont, J.: Personalized social query expansion using social bookmarking systems. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, pp. 1113–1114. ACM, New York (2011)Google Scholar
  13. 13.
    Bouadjenek, M.R., Hacid, H., Bouzeghoub, M., Vakali, A.: PerSaDoR: personalized social document representation for improving web search. Inf. Sci. 369, 614–633 (2016)CrossRefGoogle Scholar
  14. 14.
    Bouadjenek, M.R., Sanner, S., Ferraro, G.: A study of query reformulation for patent prior art search with partial patent applications. In: Proceedings of the 15th International Conference on Artificial Intelligence and Law, ICAIL 2015, pp. 23–32. ACM, New York (2015)Google Scholar
  15. 15.
    Bouadjenek, M.R., Verspoor, K.: Multi-field query expansion is effective for biomedical dataset retrieval. Database 2017, bax062 (2017)Google Scholar
  16. 16.
    Carmel, D., Roitman, H., Yom-Tov, E.: Social bookmark weighting for search and recommendation. VLDB J. 19(6), 761–775 (2010)CrossRefGoogle Scholar
  17. 17.
    Carmel, D., et al.: Personalized social search based on the user’s social network. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, pp. 1227–1236. ACM, New York (2009)Google Scholar
  18. 18.
    De, A., Diaz, E.E., Raghavan, V.V.: On fuzzy result merging for metasearch. In: 2007 IEEE International Fuzzy Systems Conference, pp. 1–6, July 2007Google Scholar
  19. 19.
    Efthimiadis, E.N.: Query expansion. In: Annual Review of Information Systems and Technology (ARIST) (1996)Google Scholar
  20. 20.
    Goh, D., Foo, S.: Social Information Retrieval Systems: Emerging Technologies and Applications for Searching the Web Effectively. Information Science Reference - Imprint of: IGI Publishing (2007)Google Scholar
  21. 21.
    Hammond, T., Hannay, T., Lund, B., Scott, J.: Social bookmarking tools: a general review. D-Lib Mag. 11(4) (2005). http://www.citeulike.org/group/684/article/80546
  22. 22.
    Hotho, A., Jäschke, R., Schmitz, C., Stumme, G.: Information retrieval in folksonomies: search and ranking. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 411–426. Springer, Heidelberg (2006).  https://doi.org/10.1007/11762256_31CrossRefGoogle Scholar
  23. 23.
    Hung, C.-C., Huang, Y.-C., Hsu, J.Y., Wu, D.K.: Tag-based user profiling for social media recommendation. In: Workshop on Intelligent Techniques for Web Personalization and Recommender Systems at AAAI 2008, Chicago, Illinois (2008)Google Scholar
  24. 24.
    Jin, S., Lin, H., Su, S.: Query expansion based on folksonomy tag co-occurrence analysis. In: 2009 IEEE International Conference on Granular Computing, pp. 300–305, August 2009Google Scholar
  25. 25.
    Krause, B., Hotho, A., Stumme, G.: A comparison of social bookmarking with traditional search. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 101–113. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-78646-7_12CrossRefGoogle Scholar
  26. 26.
    Lin, Y., Lin, H., Jin, S., Ye, Z.: Social annotation in query expansion: a machine learning approach. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, pp. 405–414. ACM, New York (2011)Google Scholar
  27. 27.
    Lioma, C., Blanco, R., Moens, M.-F.: A logical inference approach to query expansion with social tags. In: Azzopardi, L., et al. (eds.) ICTIR 2009. LNCS, vol. 5766, pp. 358–361. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-04417-5_39CrossRefGoogle Scholar
  28. 28.
    Lund, B., Hammond, T., Hannay, T., Flack, M.: Social bookmarking tools (ii): a case study - connotea. D-Lib Mag. 11(4) (2005). https://dblp.org/pers/hd/h/Hammond:Tony
  29. 29.
    Mantrach, A., Renders, J.-M.: A general framework for people retrieval in social media with multiple roles. In: Baeza-Yates, R., et al. (eds.) ECIR 2012. LNCS, vol. 7224, pp. 512–516. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-28997-2_53CrossRefGoogle Scholar
  30. 30.
    Markines, B., Cattuto, C., Menczer, F., Benz, D., Hotho, A., Stumme, G.: Evaluating similarity measures for emergent semantics of social tagging. In: Proceedings of the 18th International Conference on World Wide Web, WWW 2009, pp. 641–650. ACM, New York (2009)Google Scholar
  31. 31.
    Mei, Q., Jiang, J., Su, H., Zhai, C.: Searching and tagging: two sides of the same coin? Technical report, University of Illinois at UrbanaChampaign (2007)Google Scholar
  32. 32.
    Metzler, D., Croft, W.B.: A Markov random field model for term dependencies. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2005, pp. 472–479. ACM, New York (2005)Google Scholar
  33. 33.
    Mika, P.: Ontologies are us: a unified model of social networks and semantics. Web Semant. 5(1), 5–15 (2007)CrossRefGoogle Scholar
  34. 34.
    Nielsen, J.: Participation inequality: Encouraging more users to contribute (2006)Google Scholar
  35. 35.
    Noll, M.G., Meinel, C.: Web search personalization via social bookmarking and tagging. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 367–380. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-76298-0_27CrossRefGoogle Scholar
  36. 36.
    Porter, M.F.: An Algorithm for Suffix Stripping, pp. 313–316. Morgan Kaufmann Publishers Inc., San Francisco (1997)Google Scholar
  37. 37.
    Schenkel, R., et al.: Efficient top-k querying over social-tagging networks. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, pp. 523–530. ACM, New York (2008)Google Scholar
  38. 38.
    Schifanella, R., Barrat, A., Cattuto, C., Markines, B., Menczer, F.: Folks in folksonomies: social link prediction from shared metadata. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, WSDM 2010, pp. 271–280. ACM, New York(2010)Google Scholar
  39. 39.
    Srikanth, M., Srihari, R.: Biterm language models for document retrieval. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2002, pp. 425–426. ACM, New York (2002)Google Scholar
  40. 40.
    Stoyanovich, J., Amer-Yahia, S., Marlow, C., Yu, C.: Leveraging tagging to model user interests in del.icio.us. In: AAAI Spring Symposium: Social Information Processing, pp. 104–109 (2008)Google Scholar
  41. 41.
    Vallet, D., Cantador, I., Jose, J.M.: Personalizing web search with folksonomy-based user and document profiles. In: Gurrin, C., et al. (eds.) ECIR 2010. LNCS, vol. 5993, pp. 420–431. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-12275-0_37CrossRefGoogle Scholar
  42. 42.
    Wetzker, R., Zimmermann, C., Bauckhage, C.: Analyzing social bookmarking systems: a del.icio.us cookbook. In: Proceedings of the ECAI 2008 Mining Social Data Workshop, ECAI 2008 (2008)Google Scholar
  43. 43.
    Xu, S., Bao, S., Fei, B., Su, Z., Yu, Y.: Exploring folksonomy for personalized search. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, pp. 155–162. ACM, New York (2008)Google Scholar
  44. 44.
    Zhou, D., Wu, X., Zhao, W., Lawless, S., Liu, J.: Query expansion with enriched user profiles for personalized search utilizing folksonomy data. IEEE Trans. Knowl. Data Eng. (2017)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Mohamed Reda Bouadjenek
    • 1
  • Hakim Hacid
    • 2
  • Mokrane Bouzeghoub
    • 3
  1. 1.Department of Mechanical and Industrial EngineeringUniversity of TorontoTorontoCanada
  2. 2.Zayed UniversityDubaiUnited Arab Emirates
  3. 3.University of VersaillesVersaillesFrance

Personalised recommendations