A Causal View for Multi-Interest User Modeling in News Recommendation

Abstract

Personalized news recommendations are challenging due to the huge number of daily articles. While deep learning has achieved success in news recommendations, methods in the past often overlook the diversity of users' preferences. Recent works have explored multi-interest models to address this limitation. However, interests have different effects on click behavior, and directly modeling the matching between interests and candidates leads to the issue of spurious correlations. Specifically, when highly correlated interests obscure the true motivation for clicking, the model is unable to distinguish the interest that actually caused the click. To address this problem, this paper re-models the relationship between interests and click behavior from a causal perspective. Our proposed Counterfactual Weighted method for user M ulti-Interest modeling (CWMI) consists of a disentangled multi-interest extractor and an interest re-weighting module. Specifically, we first model the effect of interest on click behavior from a causal perspective. Then, we learn the disentangled user interests that only incorporate information from the currently clustered news. Finally, in the counterfactual world, we intervene with the current interest and re-weight it by comparing the changes in the ranking of candidates. We learned about the evolution of interest over time additionally. Experimental results on real-world news datasets demonstrate the effectiveness of the proposed methods, including disentangling interests and identifying the real interest that motivates clicks.