Skip to main content
SpringerLink
Log in

Interest Representation, Enrichment, Dynamics, and Propagation: A Study of the Synergetic Effect of Different User Modeling Dimensions for Personalized Recommendations on Twitter

  • Conference paper
  • First Online:
Knowledge Engineering and Knowledge Management (EKAW 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10024))

Included in the following conference series:

Abstract

Microblogging services such as Twitter have been widely adopted due to the highly social nature of interactions they have facilitated. With the rich information generated by users on these services, user modeling aims to acquire knowledge about a user’s interests, which is a fundamental step towards personalization as well as recommendations. To this end, researchers have explored different dimensions such as (1) Interest Representation, (2) Content Enrichment, (3) Temporal Dynamics of user interests, and (4) Interest Propagation using semantic information from a knowledge base such as DBpedia. However, those dimensions of user modeling have largely been studied separately, and there is a lack of research on the synergetic effect of those dimensions for user modeling. In this paper, we address this research gap by investigating 16 different user modeling strategies produced by various combinations of those dimensions. Different user modeling strategies are evaluated in the context of a personalized link recommender system on Twitter. Results show that Interest Representation and Content Enrichment play crucial roles in user modeling, followed by Temporal Dynamics. The user modeling strategy considering Interest Representation, Content Enrichment and Temporal Dynamics provides the best performance among the 16 strategies. On the other hand, Interest Propagation has little effect on user modeling in the case of leveraging a rich Interest Representation or considering Content Enrichment.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.twitter.com.

  2. 2.

    http://wiki.dbpedia.org.

  3. 3.

    The prefix dbpedia denotes http://dbpedia.org/resource/:The_Black_Keys.

  4. 4.

    http://goo.gl/uewQLu.

  5. 5.

    http://marketingrelevance.com/news/04/tweet-interesting-information/.

  6. 6.

    http://goo.gl/RGC16n.

  7. 7.

    The prefix dbpedia-owl denotes http://dbpedia.org/ontology/.

  8. 8.

    https://wordnet.princeton.edu/.

  9. 9.

    The prefix dbf denotes http://dbpedia.org/resource/Category:.

  10. 10.

    http://aylien.com.

  11. 11.

    http://spotlight.dbpedia.org/rest/annotate, the web service was not accessible at the time of writing this paper.

  12. 12.

    https://tagme.d4science.org/tagme/.

  13. 13.

    http://www.alchemyapi.com/.

  14. 14.

    https://www.swarmapp.com.

  15. 15.

    http://www.sussex.ac.uk/its/pdfs/SPSS_Bootstrapping_22.pdf.

  16. 16.

    70 % of one million tweets from U.S. West Coast included links. http://tnw.to/s3R2i.

References

  1. Abdel-Hafez, A., Xu, Y.: A survey of user modelling in social media websites. Comput. Inf. Sci. 6(4), 59 (2013)

    Google Scholar 

  2. Abel, F., Gao, Q., Houben, G.J., Tao, K.: Analyzing temporal dynamics in Twitter profiles for personalized recommendations in the social web. In: Proceedings of 3rd International Web Science Conference, p. 2. ACM (2011)

    Google Scholar 

  3. Abel, F., Gao, Q., Houben, G.-J., Tao, K.: Analyzing user modeling on Twitter for personalized news recommendations. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 1–12. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  4. Abel, F., Gao, Q., Houben, G.-J., Tao, K.: Semantic enrichment of Twitter posts for user profile construction on the social web. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011, Part II. LNCS, vol. 6644, pp. 375–389. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  5. Abel, F., Hauff, C., Houben, G.-J., Tao, K.: Leveraging user modeling on the social web with linked data. In: Brambilla, M., Tokuda, T., Tolksdorf, R. (eds.) ICWE 2012. LNCS, vol. 7387, pp. 378–385. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  6. Ahmed, A., Low, Y., Aly, M., Josifovski, V., Smola, A.J.: Scalable distributed inference of dynamic user interests for behavioral targeting. In: Proceedings of 17th International Conference on Knowledge Discovery and Data Mining, pp. 114–122. ACM (2011)

    Google Scholar 

  7. Budak, C., Kannan, A., Agrawal, R., Pedersen, J.: Inferring user interests from microblogs. Technical report (2014)

    Google Scholar 

  8. Degemmis, M., Lops, P., Semeraro, G.: A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation. User Model. User-Adap. Inter. 17(3), 217–255 (2007)

    Article  Google Scholar 

  9. Gauch, S., Speretta, M., Chandramouli, A., Micarelli, A.: User profiles for personalized information access. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 54–89. Springer, Heidelberg (2007). doi:10.1007/978-3-540-72079-9_2

    Chapter  Google Scholar 

  10. Harvey, M., Crestani, F., Carman, M.J.: Building user profiles from topic models for personalised search. In: Proceedings of 22nd ACM International Conference on Information and Knowledge Management, pp. 2309–2314 (2013)

    Google Scholar 

  11. Jain, P., Kumaraguru, P., Joshi, A.: @i seek ‘fb.me’: identifying users across multiple online social networks. In: Proceedings of 22nd International Conference on World Wide Web companion, pp. 1259–1268. ACM (2013)

    Google Scholar 

  12. Kapanipathi, P., Jain, P., Venkataramani, C., Sheth, A.: User interests identification on Twitter using a hierarchical knowledge base. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 99–113. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  13. Kinsella, S., Wang, M., Breslin, J.G., Hayes, C.: Improving categorisation in social media using hyperlinks to structured data sources. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011, Part II. LNCS, vol. 6644, pp. 390–404. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  14. Locke, B.W.: Named entity recognition: adapting to microblogging. Ph.D. thesis (2009)

    Google Scholar 

  15. Lu, C., Lam, W., Zhang, Y.: Twitter user modeling and tweets recommendation based on Wikipedia concept graph. In: Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence (2012)

    Google Scholar 

  16. Michelson, M., Macskassy, S.A.: Discovering users’ topics of interest on Twitter: a first look. In: Proceedings of Fourth Workshop on Analytics for Noisy Unstructured Text Data, pp. 73–80. ACM (2010)

    Google Scholar 

  17. Mislove, A., Viswanath, B., Gummadi, K.P., Druschel, P.: You are who you know: inferring user profiles in online social networks. In: Proceedings of Third ACM International Conference on Web Search and Data Mining, pp. 251–260. ACM (2010)

    Google Scholar 

  18. Nishioka, C., Scherp, A.: Profiling vs. time vs. content: what does matter for top-k publication recommendation based on Twitter profiles? In: Proceedings of 16th ACM/IEEE-CS on Joint Conference on Digital Libraries, JCDL 2016, pp. 171–180. ACM, New York (2016)

    Google Scholar 

  19. Orlandi, F., Breslin, J., Passant, A.: Aggregated, interoperable and multi-domain user profiles for the social web. In: Proceedings of 8th International Conference on Semantic Systems, pp. 41–48. ACM (2012)

    Google Scholar 

  20. Piao, G.: Towards comprehensive user modeling on the social web for personalized link recommendations. In: Proceedings of 2016 Conference on User Modeling Adaptation and Personalization, UMAP 2016, pp. 333–336. ACM (2016)

    Google Scholar 

  21. Piao, G.: User modeling on Twitter with WordNet Synsets and DBpedia concepts for personalized recommendations. In: The 25th ACM International Conference on Information and Knowledge Management. ACM (2016)

    Google Scholar 

  22. Piao, G., Breslin, J.G.: Analyzing aggregated semantics-enabled user modeling on Google+ and Twitter for personalized link recommendations. In: User Modeling, Adaptation, and Personalization, pp. 105–109. ACM (2016)

    Google Scholar 

  23. Piao, G., Breslin, J.G.: Exploring dynamics and semantics of user interests for user modeling on Twitter for link recommendations. In: 12th International Conference on Semantic Systems, pp. 81–88. ACM (2016)

    Google Scholar 

  24. Zarrinkalam, F., Kahani, M.: Semantics-enabled user interest detection from Twitter. In: 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (2015)

    Google Scholar 

Download references

Acknowledgments

This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 (Insight Centre for Data Analytics).

Author information

Authors and Affiliations

  1. Insight Centre for Data Analytics, NUI Galway, IDA Business Park, Lower Dangan, Galway, Ireland

    Guangyuan Piao & John G. Breslin

Authors
  1. Guangyuan Piao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John G. Breslin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guangyuan Piao .

Editor information

Editors and Affiliations

  1. Linköping University, Linköping, Sweden

    Eva Blomqvist

  2. University of Bologna, Bologna, Italy

    Paolo Ciancarini

  3. University of Bologna, Bologna, Italy

    Francesco Poggi

  4. University of Bologna, Bologna, Italy

    Fabio Vitali

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Piao, G., Breslin, J.G. (2016). Interest Representation, Enrichment, Dynamics, and Propagation: A Study of the Synergetic Effect of Different User Modeling Dimensions for Personalized Recommendations on Twitter. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds) Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science(), vol 10024. Springer, Cham. https://doi.org/10.1007/978-3-319-49004-5_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-49004-5_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49003-8

  • Online ISBN: 978-3-319-49004-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation