HDNR: A Hyperbolic-Based Debiased Approach for Personalized News Recommendation
Shicheng Wang, Shu Guo, Lihong Wang, Tingwen Liu, Hongbo Xu · 2023
Personalized news recommendation aims to recommend candidate news to the target user, according to the clicked news history. The user-news interaction data exhibits power-law distribution, however, existing works usually learn representations in Euclidean space which makes inconsistent capacities between data space and embedding space, leading to severe representation distortion problem. Besides, the existence of conformity bias, a potential cause of power-law distribution, may introduce biased guidance to learn user representations. In this paper, we propose a novel debiased method based on hyperbolic space, named HDNR, to tackle the above problems. Specifically, first, we employ hyperboloid model with exponential growth capacity to conduct user and news modeling, in order to solve inconsistent space capacities problem and obtain low distortion representations. Second, we design a re-weighting aggregation module to further mitigate conformity bias in data distribution, through considering local importance of the clicked news among contextual history and its global popularity degree simultaneously. Finally, we calculate the relevance score between target user and candidate news representations. We conduct experiments on two real-world news recommendation datasets MIND-Large, MIND-Small and empirical results demonstrate the effectiveness of our approach from multiple perspectives.