Balancing the popularity bias of object similarities for personalised recommendation

Hou, L., Pan, X. and Liu, K. (2018) Balancing the popularity bias of object similarities for personalised recommendation. The European Physical Journal B, 91 (3). 47. ISSN 1434-6036 doi: 10.1140/epjb/e2018-80374-8

Abstract/Summary

Network-based similarity measures have found wide applications in recommendation algorithms and made signicant contributions for uncovering users' potential interests. However, existing measures are generally biased in terms of popularity, that the popular objects tend to have more common neighbours with others and thus are considered more similar to others. Such popularity bias of similarity quantification will result in the biased recommendations, with either poor accuracy or poor diversity. Based on the bipartite network modelling of the user-object interactions, this paper firstly calculates the expected number of common neighbours of two objects with given popularities in random networks. A Balanced Common Neighbour similarity index is accordingly developed by removing the random-driven common neighbours, estimated as the expected number, from the total number. Recommendation experiments in three data sets show that balancing the popularity bias in a certain degree can significantly improve the recommendations' accuracy and diversity simultaneously.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/76569
Identification Number/DOI	10.1140/epjb/e2018-80374-8
Refereed	Yes
Divisions	Henley Business School > Digitalisation, Marketing and Entrepreneurship
Publisher	Springer
Download/View statistics	View download statistics for this item