Balancing the popularity bias of object similarities for personalised recommendation
Informatics Research Centre, Henley Business School, University of Reading, Reading,
RG6 6UD, UK
2 Data Science and Cloud Service Research Centre, Shanghai University of Finance and Economics, Shanghai 200433, P.R. China
a e-mail: email@example.com
Received in final form: 18 October 2017
Published online: 7 March 2018
Network-based similarity measures have found wide applications in recommendation algorithms and made significant 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.
Key words: Statistical and Nonlinear Physics
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