Alleviating the recommendation bias via rank aggregation

Qiang Dong, Quan Yuan and Yang-Bo Shi

Physica A: Statistical Mechanics and its Applications, 2019, vol. 534, issue C

Abstract: The primary goal of a recommender system is often known as “helping users find relevant items”, and a lot of recommendation algorithms are proposed accordingly. However, these accuracy-oriented methods usually suffer the problem of recommendation bias on popular items, which is not welcome to not only users but also item providers. To alleviate the recommendation bias problem, we propose a generic rank aggregation framework for the recommendation results of an existing algorithm, in which the user- and item-oriented ranking results are linearly aggregated together, with a parameter controlling the weight of the latter ranking process. Experiment results of a typical algorithm on two real-world data sets show that, this framework is effective to improve the recommendation fairness of any existing accuracy-oriented algorithms, while avoiding significant accuracy loss.

Keywords: Recommender systems; Popularity bias; Rank aggregation; Gini coefficient (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:534:y:2019:i:c:s0378437119312051

DOI: 10.1016/j.physa.2019.122073

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