This content originally appeared on HackerNoon and was authored by Tech Media Bias [Research Publication]
Table of Links
3 End-to-End Adaptive Local Learning
3.1 Loss-Driven Mixture-of-Experts
3.2 Synchronized Learning via Adaptive Weight
4 Debiasing Experiments and 4.1 Experimental Setup
4.3 Ablation Study
4.4 Effect of the Adaptive Weight Module and 4.5 Hyper-parameter Study
6 Conclusion, Acknowledgements, and References
6 Conclusion
In this study, we aim to address the mainstream bias in recommender systems that niche users who possess special and minority interests receive overly low utility from recommendation models. We identify two root causes of this bias: the discrepancy modeling problem and the unsynchronized learning problem. Toward debiasing, we devise an end-to-end adaptive local learning framework: we first propose a loss-driven Mixture-of-Experts module to counteract the discrepancy modeling problem, and then we develop an adaptive weight module to fight against the unsynchronized learning problem. Extensive experiments show the outstanding performance of our proposed method on both niche and mainstream users and overall performance compared to SOTA alternatives.
\ Acknowledgements. This research was funded in part by 4-VA, a collaborative partnership for advancing the Commonwealth of Virginia.
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:::info Authors:
(1) Jinhao Pan [0009 −0006 −1574 −6376], Texas A&M University, College Station, TX, USA;
(2) Ziwei Zhu [0000 −0002 −3990 −4774], George Mason University, Fairfax, VA, USA;
(3) Jianling Wang [0000 −0001 −9916 −0976], Texas A&M University, College Station, TX, USA;
(4) Allen Lin [0000 −0003 −0980 −4323], Texas A&M University, College Station, TX, USA;
(5) James Caverlee [0000 −0001 −8350 −8528]. Texas A&M University, College Station, TX, USA.
:::
:::info This paper is available on arxiv under CC BY 4.0 DEED license.
:::
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This content originally appeared on HackerNoon and was authored by Tech Media Bias [Research Publication]
Tech Media Bias [Research Publication] | Sciencx (2024-08-21T21:00:21+00:00) Countering Mainstream Bias via End-to-End Adaptive Local Learning: Conclusion and References. Retrieved from https://www.scien.cx/2024/08/21/countering-mainstream-bias-via-end-to-end-adaptive-local-learning-conclusion-and-references/
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