Alleviating the recommendation bias via rank aggregation.

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. • In the recommendation results of common algorithms, the direct rank number of a user-item pair usually shows a negative correlation with the item degree. • We propose the Two-Way Rank Aggregation framework of the direct and reversed rank results. • Our proposed framework can improved existing algorithms with superior coverage and comparable accuracy and diversity. [ABSTRACT FROM AUTHOR]