Alleviating the recommendation bias via rank aggregation
This work addresses bias issues in recommender systems for users and item providers, but it is incremental as it builds on existing algorithms.
The paper tackles the problem of recommendation bias towards popular items in recommender systems by proposing a generic rank aggregation framework that combines user- and item-oriented rankings. Experimental results on two real-world datasets show the framework effectively improves fairness without significant accuracy loss.
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.