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The primary goal of a recommender system is often known as "helping usersfind relevant items", and a lot of recommendation algorithms are proposedaccordingly. However, these accuracy-oriented methods usually suffer theproblem of recommendation bias on popular items, which is not welcome to notonly users but also item providers. To alleviate the recommendation biasproblem, we propose a generic rank aggregation framework for the recommendationresults of an existing algorithm, in which the user- and item-oriented rankingresults are linearly aggregated together, with a parameter controlling theweight of the latter ranking process. Experiment results of a typical algorithmon two real-world data sets show that, this framework is effective to improvethe recommendation fairness of any existing accuracy-oriented algorithms, whileavoiding significant accuracy loss.

Alleviating the recommendation bias via rank aggregation