Softening overly demanding requirements in recommendation system

Haoyu Hu; Jinyi Guo; Victor Li; Yuzhang Li

doi:10.1117/12.2683667

26 June 2023 Softening overly demanding requirements in recommendation system

Haoyu Hu, Jinyi Guo, Victor Li, Yuzhang Li

Proceedings Volume 12721, Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023); 127210N (2023) https://doi.org/10.1117/12.2683667
Event: 2023 2nd International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2023, Chengdu, China

Abstract

Used in multiple industries, recommender systems enhance the user experience by creating personalized item suggestions. However, without bias awareness, the system could output unfavorable results, leading to the item under-recommendation bias. To decrease this type of bias, creating a fair top-k list (i.e., list of top-ranked items by the user) is needed. Based on the debiased personalized ranking model (DPR) proposed in the works of Zhu et al., the paper aims to reduce item under-recommendation bias in recommender systems in the Bayesian Personalized Ranking (BPR) recommendation model. This is done by applying several fairness metrics, creating fairness through an adversarial component, and using an autoencoder on the data. The advantages of the new proposed model are proven through two sets of experiments based on multiple datasets and baselines, respectively. This results in a solution that is dozens of times more efficient compared to previously proposed ones.

Citation Download Citation

Haoyu Hu, Jinyi Guo, Victor Li, and Yuzhang Li "Softening overly demanding requirements in recommendation system", Proc. SPIE 12721, Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 127210N (26 June 2023); https://doi.org/10.1117/12.2683667

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