Skip to main content
SpringerLink
Log in

Personalized Search by a Multi-type and Multi-level User Profile in Folksonomy

  • Research Article - Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

With the development of the web 2.0 communities, more and more collaborative tagging systems become popular in recent years. Based on previous relevant works on the collaborative tagging system, this paper proposes a concept of a multi-type and multi-level user profile for improving the efficiency of personalized search. User profile consists of different types of resource attributes, and every type reflects multi-level favorites and nuisances from user. A detailed design process of user profile is presented in this paper. We propose a personalized search method by using the multi-type and multi-level user profile. Experimental results on a large real dataset demonstrate that the multi-type and multi-level user profile outperforms the baseline methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Matthijs, N.; Radlinski, F.: Personalizing Web Search using Long Term Browsing History. In: Proceedings of the 4th International Conference on Web Search and Web Data Mining, pp. 25–34. ACM Press, Hong Kong, China (2011)

  2. Bin, T.; Xue, S.; Cheng, Z.: Mining long-term search history to improve search accuracy. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 718–723. ACM Press, Philadelphia, USA (2006)

  3. Vallet, D.; Cantador, I.; Jose, J.M.: Personalizing web search with folksonomy-based user and document profiles. In: Proceedings of the 32nd European conference on Advances in Information Retrieval, pp. 420–431. ACM Press, Milton Keynes, UK (2010)

    Google Scholar 

  4. Xie, H.; Li, X.; Wang, T.; Lau, R.Y.K.; Wong, T.; Chen, L.; Wang, F.L.; Li, Q.: Incorporating sentiment into tag-based user profiles and resource profiles for personalized search in folksonomy. Inf. Process. Manag. 52, 61–72 (2016)

    Article  Google Scholar 

  5. Abel, F.; Baldoni, M.; Baroglio, C.; Henze, N.; Kawase, R.; Krause, D.; Patti, V.: Leveraging search and content exploration by exploiting context in folksonomy systems. New Rev. Hypermed. Multimed. 16(1), 33–70 (2010)

    Article  Google Scholar 

  6. Al-Khalifa, H.S.; Davis, H.C.: Measuring the semantic value of folksonomies. In: Proceedings of the Conference on Innovations in Information Technology, pp. 1–5. IEEE Press, Dubai, United Arab Emirates (2006)

  7. Xie, H.; Li, Q.; Cai, Y.: Community-aware resource profile for personalized search in folksonomy. J. Comput. Sci. Technol. 27(3), 599–610 (2012)

    Article  Google Scholar 

  8. Cai, Y.; Li, Q.; Xie, H.; Min, H.: Exploring personalized searches using tag-based user profiles and resource profiles in folksonomy. Neural Netw. 58(10), 98–110 (2014)

    Article  Google Scholar 

  9. Noll, M.G.; Meinel, C.: Web search personalization via social bookmarking and tagging. In: Proceedings of the 6th international the Semantic Web and 2nd Asian Conference on Asian Semantic Web Conference, pp. 367–380. ACM Press, Berlin, Heidelberg (2007)

    Chapter  Google Scholar 

  10. Cai, Y.; Li, Q.; Xie, H.; Yu, L.: Personalized resource search by tag-based user profile and resource profile. In: Proceedings of the 11th International Conference on Web Information Systems Engineering, pp. 510–523. ACM Press, Hong Kong, China (2010)

    Chapter  Google Scholar 

  11. Robertson, S.E.; Walker, S.: Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 232–241. ACM press, Dublin, Ireland (1994)

    Chapter  Google Scholar 

  12. Cai, Y.; Li, Q.: Personalized search by tag-based user profile and resource profile in collaborative tagging systems. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 969–978. ACM Press, Toronto, Canada (2010)

  13. Choy, S.; Lui, A.K.: Web information retrieval in collaborative tagging systems. In: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 352–355. IEEE Press, Hong Kong, China (2007)

  14. Morrison, P.J.: Tagging and searching: Search retrieval efficiency of folksonomies on the World Wide Web. Inf. Process. Manag. 44, 1562–1579 (2008)

    Article  Google Scholar 

  15. 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, pp. 155–162. ACM Press, Singapore (2008)

  16. Jin, S.; Lin, H.; Su, S.: Query expansion based on folksonomy tag co-occurrence analysis. In: Proceedings of the 2009 IEEE International Conference on Granular Computing, pp. 300–305. IEEE Press, Nanchang, China (2009)

  17. Hsieh, W.T.; Stu, J.; Chen, Y.L.; Chou, S.C.T.: A collaborative desktop tagging system for group knowledge management based on concept space. Expert Syst. Appl. 36(5), 9513–9523 (2009)

    Article  Google Scholar 

  18. Font, F.; Serra, J.; Serra, X.: Folksonomy-based tag recommendation for collaborative tagging systems. Int. J. Semant. Web Inf. 9(2), 1–30 (2013)

    Article  Google Scholar 

  19. Wang, S.; Wang, W.; Zhuang, Y.; Fei, X.: An ontology evolution method based on folksonomy. J. App. Res. Technol. 13(2), 177–187 (2015)

    Article  Google Scholar 

  20. Li, L.; Zhang, C.: Characterizing users tagging behavior in academic blogs. In: Proceedings of the 2016 IEEE/ACM Joint Conference on Digital Libraries, pp. 215–216. IEEE Press, Newark, USA (2016)

  21. Pandya, S.D.; Virparia, P.V.; Chavda, R.: Implementation of folksonomy based tag cloud model for information retrieval from document repository in an Indian university. Int. J. Soft Comput. Artif. Intell. 5(1), 9–15 (2016)

    Google Scholar 

  22. Godoy, D.; Corbellini, A.: Folksonomy-based recommender systems: a state-of-the-art review. Int. J. Intell. Syst. 31(4), 314–346 (2016)

    Article  Google Scholar 

  23. Zhang, Y.; Song, W.: A collaborative filtering recommendation algorithm based on item genre and rating similarity. In: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing, pp. 72–75. IEEE Press, Wuhan, China (2009)

  24. Kim, K.; Moon, N.: Recommender system design using movie genre similarity and preferred genres in smartphone. Multimed. Tools Appl. 61(1), 87–104 (2012)

    Article  Google Scholar 

  25. Ashkezari-T, S.; Akbarzadeh-T, M.: Fuzzy-bayesian network approach to genre-based recommender systems. In: Proceedings of the 2010 IEEE International Conference on Fuzzy Systems, Barcelona, vol. 23(3), pp. 1–7. IEEE, Spain (2010)

  26. Bansal, S.; Gupya, C.; Arora A.: User tweets based genre prediction and movie recommendation using LSI and SVD. In: Proceedings of the 2016 Ninth International Conference on Contemporary Computing, pp. 1–6. IEEE Press, Noida, India (2017)

  27. Choi, S.M.; Ko, S.K.; Han, Y.S.: A movie recommendation algorithm based on genre correlations. Expert Syst. Appl. 39(9), 8079–8085 (2012)

    Article  Google Scholar 

  28. Hwang, T.G.; Park, C.S.; Hong, J.H.; Kim, S.K.: An algorithm for movie classification and recommendation using genre correlation. Multimed. Tools Appl 75(20), 12843–12858 (2016)

    Article  Google Scholar 

  29. Zheng, Q.; Ip, H.H.S.: Customizable surprising recommendation based on the tradeoff between genre difference and genre similarity. In: Proceedings of the International Conferences on Web Intelligence and Intelligent Agent Technology, pp. 702–709. IEEE Press, Macau, China (2012)

  30. Frémal, S.; Lecron, F.: Weighting strategies for a recommender system using item clustering based on genres. Expert Syst. Appl. 77, 105–113 (2017)

    Article  Google Scholar 

  31. Anand, D.; Mampilli, B.: Folksonomy-based fuzzy user profile for improved recommendations. Expert Syst. Appl. 41, 2424–2436 (2014)

    Article  Google Scholar 

  32. Ma, J.; Li, G.; Zhong, M.; Zhao, X.; Zhu, L.; Li, X.: LGA: latent genre aware micro-video recommendation on social media. Multimed. Tools Appl. (2017). https://doi.org/10.1007/s11042-017-4827-2

    Article  Google Scholar 

  33. Hu, Y.; Yang, Y.; Li, C.; Li, L.: A hybrid genre-based personalized recommendation algorithm. In: Proceedings of 11th Conference on Industrial Electronics and Applications, pp. 1369–1373. IEEE Press, Hefei, China (2016)

  34. Biancalana, C.; Micarelli, A.: Social tagging in query expansion: a new way for personalized web search. In: Proceedings of the International Conference on Computational Science and Engineering, pp. 1060–1065. IEEE Press, Vancouver, Canada (2009)

  35. Tao, Z.; Hu, J.; He, W.; Li, R.; Li, D.: Modeling user’s preference in folksonomy for personalized search. In: Proceedings of the 2011 International Conference on Cloud and Service Computing, pp. 55–59. IEEE Press, Washington, USA (2011)

  36. Du, Q.; Xie, H.; Cai, Y.: Folksonomy-based personalized search by hybrid user profiles in multiple levels. Neurocomputing 204, 142–152 (2016)

    Article  Google Scholar 

  37. Kumar, H.; Lee, S.; Kim, H.G.: Exploiting social bookmarking services to build clustered user interest profile for personalized search. Inf. Sci. 381, 399–417 (2014)

    Article  Google Scholar 

  38. Kim, H.N.; Rawashdeh, M.; Alghamdi, A.; Saddik, A.E.: Folksonomy-based personalized search and ranking in social media services. Inf. Syst. 37, 61–76 (2012)

    Article  Google Scholar 

  39. Han, X.; Shen, Z.; Miao, C.; Luo, X.: Folksonomy-based ontological user interest profile modeling and its application in personalized search. In: Proceedings of the International Conference on Active Media Technology, pp. 34–46. Springer Press, Berlin, Heidelberg (2010)

  40. Zhou, D.; Lawless S.; Wu, X.; Zhao, W.; Liu, J.: Enhanced personalized search using social data. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 700–710. Austin, Texas (2017)

  41. Han, K.; Park, J.; Yi. M.Y.: Adaptive and multiple interest-aware user profiles for personalized search in folksonomy: A simple but effective graph-based profile model. In: Proceedings of the 2015 International Conference on Big Data and Smart Computing, pp. 225–231. IEEE Press, Jeju, South Korea (2015)

  42. Zenebe, A.; Norcio, A.F.: Representation, similarity measures and aggregation methods using fuzzy sets for content-based recommender systems. Fuzzy Set. Syst. 160, 76–94 (2009)

    Article  MathSciNet  Google Scholar 

  43. Yang, Y.; Hu, S.; Cai, Y.; Leung, H.; Lau, R.Y.K.: Exploring Reviews and Ratings on Reviews for Personalized Search. In: Proceedings of the 14th ICWL Conference on Web-Based Learning, pp. 140–150. Springer Press, Hong Kong, China (2015)

    Chapter  Google Scholar 

Download references

Acknowledgements

This research was partially supported by Postgraduate Research & Practice Innovation Program of Jiangsu Province of China under Grant No. KYCX17_0486, The Fundamental Research Funds for the Central Universities under Grant No. 2017B708X14, Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) of China under Grant No. MJUKF201740, Natural Science Foundation of the Colleges and Universities in Jiangsu Province of China under Grant No. 16KJB520019, Natural Science Foundation of the Colleges and Universities in Anhui Province of China under Grant No. KJ2017B016, and Natural Science Foundation of the Colleges and Universities in Anhui Province of China under Grant No. KJ2016A592.

Author information

Authors and Affiliations

  1. College of Computer and Information, Hohai University, Nanjing, Jiangsu Province, China

    Zhinan Gou, Lixin Han, Jun Zhu, Yi Yang & Baobin Duan

  2. College of Computer Science, Nanjing University of Science and Technology Zijin College, Nanjing, Jiangsu Province, China

    Jun Zhu

  3. College of Computer Science and Technology, Huaibei Normal University, Huaibei, Anhui Province, China

    Yi Yang

  4. Department of Mathematics and Physics, Hefei University, Hefei, Anhui Province, China

    Baobin Duan

Authors
  1. Zhinan Gou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lixin Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yi Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Baobin Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhinan Gou.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gou, Z., Han, L., Zhu, J. et al. Personalized Search by a Multi-type and Multi-level User Profile in Folksonomy. Arab J Sci Eng 43, 7563–7572 (2018). https://doi.org/10.1007/s13369-018-3133-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-018-3133-2

Keywords

Search

Navigation