Skip to main content
SpringerLink
Log in

Information Retrieval Models for Contact Recommendation in Social Networks

  • Conference paper
  • First Online:
Advances in Information Retrieval (ECIR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11437))

Included in the following conference series:

Abstract

The fast growth and development of online social networks has posed new challenges for information retrieval and, as a particular case, recommender systems. A particularly compelling problem in this context is recommending network edges, that is, automatically predicting people that a given user may wish or benefit from connecting to in the network. This task has interesting particularities compared to more traditional recommendation domains, a salient one being that recommended items belong to the same space as the users they are recommended to. In this paper, we investigate the connection between the contact recommendation and the text retrieval tasks. Specifically, we research the adaptation of IR models for recommending contacts in social networks. We report experiments over data downloaded from Twitter where we observe that IR models, particularly BM25, are competitive compared to state-of-the art contact recommendation methods. We further find that IR models have additional advantages in computational efficiency, and allow for fast incremental updates of recommendations as the network grows.

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 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
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

References

  1. Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)

    Article  Google Scholar 

  2. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)

    Article  Google Scholar 

  3. Aiello, L., Barbieri, N.: Evolution of ego-networks in social media with link recommendations. In: 10th ACM International Conference on Web Search and Data Mining (WSDM 2017), pp. 111–120. ACM, New York (2017)

    Google Scholar 

  4. Backstrom, L., Leskovec, J.: Supervised random walks: predicting and recommending links in social networks. In: 4th ACM International Conference on Web Search and Data Mining (WSDM 2011), pp. 635–644. ACM, New York (2011)

    Google Scholar 

  5. Baeza-Yates, R., Ribeiro-Neto, B.: Modern information retrieval: the concepts and technology behind search, 2nd edn. Addison-Wesley, Harlow (2011)

    Google Scholar 

  6. Belkin, N.J., Croft, W.B.: Information filtering and information retrieval: two sides of the same coin? Commun. ACM 35(12), 29–38 (1992)

    Article  Google Scholar 

  7. Bellogín, A., Wang, J., Castells, P.: Bridging memory-based collaborative filtering and text retrieval. Inf. Retrieval 16(6), 697–724 (2013)

    Article  Google Scholar 

  8. Büttcher, S., Clarke, C.L.A., Cormack, G.V.: Information retrieval: implementing and evaluating search engines. MIT Press, Cambridge (2010)

    MATH  Google Scholar 

  9. Castells, P., Hurley, N.J., Vargas, S.: Novelty and Diversity in Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 881–918. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7637-6_26

    Chapter  Google Scholar 

  10. Goel, A., Gupta, P., Sirois, J., Wang, D., Sharma, A., Gurumurthy, S.: The who-to-follow system at twitter: strategy, algorithms and revenue impact. Interfaces 45(1), 98–107 (2015)

    Article  Google Scholar 

  11. Goel, A., Gupta, P., Sirois, J., Wang, D., Sharma, A., Gurumurthy, S.: WTF: the who to follow service at Twitter. In: 22nd International Conference on World Wide Web (WWW 2013), pp. 505–514. ACM, New York (2013)

    Google Scholar 

  12. Guy, I.: Social Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, 2nd edn, pp. 511–543. Springer, New York (2015)

    Chapter  Google Scholar 

  13. Hannon, J., Bennet, M., Smyth, B.: Recommending Twitter users to follow using content and collaborative filtering approaches. In: 4th ACM conference on Recommender Systems (RecSys 2010), pp. 199–206. ACM, New York (2010)

    Google Scholar 

  14. Al Hasan, M., Chaoji, V., Salem, S., Zaki, M.: Link prediction using supervised learning. In: SDM 06 Workshop on Link Analysis, Counterterrorism and Security at the 5th SIAM International Conference of Data Mining (2006)

    Google Scholar 

  15. Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: 8th IEEE International Conference on Data Mining (ICDM 2008), pp. 263–272, IEEE Press, New York (2008)

    Google Scholar 

  16. Jaccard, P.: Étude de la distribution florale dans une portion des Alpes et du Jura. Bulletin de la Société Vaudoise des Sciences Naturelles 37(142), 547–579 (1901)

    Google Scholar 

  17. Jelinek, F., Mercer, R.L.: Interpolated estimation of Markov source parameters from sparse data. In: Gelsema, E.S., Kanal, L.N. (eds.) Pattern Recognition in Practice, pp. 381–397. North-Holland, Amsterdam (1980)

    Google Scholar 

  18. Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inform. Sci. Technol. 58(7), 1019–1031 (2007)

    Article  Google Scholar 

  19. Lü, L., Zhou, T.: Link prediction in social networks: a survey. Phys. A 390(6), 1150–1170 (2010)

    Article  Google Scholar 

  20. Mackay, D., Bauman, L.: A hierarchical Dirichlet language model. Nat. Lang. Eng. 1(3), 289–307 (1995)

    Article  Google Scholar 

  21. Ning, X., Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 37–76. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7637-6_2

    Chapter  Google Scholar 

  22. Ponte, J.M., Croft, W.B.: A language modelling approach to information retrieval. In: 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 1998), pp. 275–281. ACM Press, New York (1998)

    Google Scholar 

  23. RankSys library. http://ranksys.org. Accessed 11 Oct 2018

  24. Robertson, S.E.: The probability ranking principle in IR. J. Doc. 33(4), 294–304 (1977)

    Article  Google Scholar 

  25. Robertson, S.E., Zaragoza, H.: The probabilistic relevance framework: BM25 and beyond. Found. Trends Inform. Retrieval 3(4), 333–389 (2009)

    Article  Google Scholar 

  26. Salton, G., Wong, A., Yang, C.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)

    Article  Google Scholar 

  27. Salton, G., McGill, M.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983)

    MATH  Google Scholar 

  28. Sanz-Cruzado, J., Castells, P.: Enhancing structural diversity in social networks by recommending weak ties. In: 12th ACM Conference on Recommender Systems (RecSys 2018), pp. 233–241. ACM, New York (2018)

    Google Scholar 

  29. Sanz-Cruzado, J., Pepa, S.M., Castells, P.: Structural novelty and diversity in link prediction. In: 9th International Workshop on Modelling Social Media (MSM 2018) at The Web Conference (WWW Companion 2018), pp. 1347–1351. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland (2018)

    Google Scholar 

  30. Sanz-Cruzado, J., Castells, P.: Contact recommendations in social networks. In: Berkovsky, S., Cantador, I., Tikk, D. (eds.) Collaborative recommendations: algorithms, Practical Challenges and Applications, pp. 525–577. World Scientific Publishing, Singapore (2018)

    Google Scholar 

  31. Valcarce, D.: Exploring statistical language models for recommender systems. In: 9th ACM Conference on Recommender Systems (RecSys 2015), pp. 375–378. ACM Press, New York (2015)

    Google Scholar 

  32. Valcarce, D., Parapar, J., Barreiro, A.: Axiomatic analysis of language modelling of recommender systems. Int. J. Uncertainty, Fuzziness Knowl.-based Syst. 25(Suppl. 2), 113–127 (2017)

    Article  MathSciNet  Google Scholar 

  33. White, S., Smyth, P.: Algorithms for estimating relative importance in networks. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2003), pp. 266–275. ACM, New York (2003)

    Google Scholar 

  34. Yu, T., Mengshoel, O., Jude, A., Feller, E., Forgeat, J., Radia, N.: Incremental learning for matrix factorization in recommender systems. In: 2016 IEEE Conference on Big Data (BigData 2016), pp. 1056–1063. IEEE Press, New York (2016)

    Google Scholar 

Download references

Acknowledgements

This work was funded by the Spanish Government (grant nr. TIN2016-80630-P).

Author information

Authors and Affiliations

  1. Universidad Autónoma de Madrid, Escuela Politécnica Superior, Madrid, Spain

    Javier Sanz-Cruzado & Pablo Castells

Authors
  1. Javier Sanz-Cruzado
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pablo Castells
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Javier Sanz-Cruzado or Pablo Castells .

Editor information

Editors and Affiliations

  1. University of Strathclyde, Glasgow, UK

    Leif Azzopardi

  2. Bauhaus Universität Weimar, Weimar, Germany

    Benno Stein

  3. Universität Duisburg-Essen, Duisburg, Germany

    Norbert Fuhr

  4. GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany

    Philipp Mayr

  5. Delft University of Technology, Delft, The Netherlands

    Claudia Hauff

  6. University of Twente, Enschede, The Netherlands

    Djoerd Hiemstra

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sanz-Cruzado, J., Castells, P. (2019). Information Retrieval Models for Contact Recommendation in Social Networks. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11437. Springer, Cham. https://doi.org/10.1007/978-3-030-15712-8_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-15712-8_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15711-1

  • Online ISBN: 978-3-030-15712-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation