Deconfounding User Preference in Recommendation Systems through Implicit and Explicit Feedback

Recommender systems are influenced by many confounding factors (i.e., confounders) which result in various biases (e.g., popularity biases) and inaccurate user preference. Existing approaches try to eliminate these biases by inference with causal graphs. However, they assume all confounding factors can be observed and no hidden confounders exist. We argue that many confounding factors (e.g., season) may not be observable from user-item interaction data, resulting inaccurate user preference. In this paper, we propose a deconfounded recommender considering unobservable confounders. Specifically, we propose a new causal graph with explicit and implicit feedback, which can better model user preference. Then, we realize a deconfounded estimator by the front-door adjustment, which is able to eliminate the effect of unobserved confounders. Finally, we conduct a series of experiments on two real-world datasets, and the results show that our approach performs better than other counterparts in terms of recommendation accuracy.