A Novel Causal Discovery Model for Recommendation System

Guohao Sun, Huirong Hua, Jinhu Lu, Xiu Fang

Abstract: The recommendation system is now playing a more and more important role in our daily life. Recently, some scholars proposed that human behavior is governed by a complex web of causal models, and causal relationships are crucial in the recommendation process. Unfortunately, existing methods are limited in their ability to uncover hidden causal relationships because they may generate false causal relationships, which limits their recommendation performance. To address this issue, we propose a new recommendation model that leverages the causal relationship recommendation model and integrates a causal discovery module into the recommendation process. In this way, we can capture accurately the causal relationships underlying user behavior and generate more targeted recommendations. By fitting actual user behavior data, we can learn a cause-and-effect diagram that accurately reflects the real-world dynamics of the system. Extensive experiments conducted on two real-world datasets demonstrate that our method significantly outperforms the state-of-the-art approaches.

External IDs:dblp:conf/apweb/SunHLF23