Axiomatic Analysis of Contact Recommendation Methods in Social Networks: an IR Perspective

Sanz-Cruzado, J. , Macdonald, C. , Ounis, I. and Castells, P. (2020) Axiomatic Analysis of Contact Recommendation Methods in Social Networks: an IR Perspective. In: 42nd European Conference on IR Research (ECIR 2020), Lisbon, Portugal, 14-17 Apr 2020, pp. 175-190. ISBN 9783030454388 (doi: 10.1007/978-3-030-45439-5_12)

Abstract

Contact recommendation is an important functionality in many social network scenarios including Twitter and Facebook, since they can help grow the social networks of users by suggesting, to a given user, people they might wish to follow. Recently, it has been shown that classical information retrieval (IR) weighting models – such as BM25 – can be adapted to effectively recommend new social contacts to a given user. However, the exact properties that make such adapted contact recommendation models effective at the task are as yet unknown. In this paper, inspired by new advances in the axiomatic theory of IR, we study the existing IR axioms for the contact recommendation task. Our theoretical analysis and empirical findings show that while the classical axioms related to term frequencies and term discrimination seem to have a positive impact on the recommendation effectiveness, those related to length normalization tend to be not desirable for the task.

• Item Type: Conference Proceedings
• Additional Information: J. Sanz-Cruzado and P. Castells were partially supported by the Spanish Government (TIN2016-80630-P). C. Macdonald and I. Ounis were partially supported by the European Community’s Horizon 2020 programme, under grant agreement Open image in new window 779747 entitled BigDataStack.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Sanz-Cruzado Puig, Dr Javier and Macdonald, Professor Craig and Ounis, Professor Iadh
• Authors: Sanz-Cruzado, J., Macdonald, C., Ounis, I., and Castells, P.
• College/School: College of Science and Engineering > School of Computing Science
• ISSN: 0302-9743
• ISBN: 9783030454388
• Copyright Holders: Copyright © 2020 Springer Nature Switzerland AG
• First Published: First published in Lecture Notes in Computer Science 12035:175-190
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

Related URLs

• Organisation