Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue

TL;DR

This paper introduces a counterfactual inference approach within a causal graph framework to mitigate clickbait effects in recommendation systems, thereby improving user satisfaction beyond traditional CTR optimization.

Contribution

It proposes a novel counterfactual recommendation method that reduces clickbait bias by modeling causal relationships and estimating click likelihood in a counterfactual scenario.

Findings

Significantly improves user satisfaction in real-world datasets

Reduces the impact of clickbait on recommendation quality

Enhances the robustness of CTR models against misleading clicks

Abstract

Recommendation is a prevalent and critical service in information systems. To provide personalized suggestions to users, industry players embrace machine learning, more specifically, building predictive models based on the click behavior data. This is known as the Click-Through Rate (CTR) prediction, which has become the gold standard for building personalized recommendation service. However, we argue that there is a significant gap between clicks and user satisfaction -- it is common that a user is "cheated" to click an item by the attractive title/cover of the item. This will severely hurt user's trust on the system if the user finds the actual content of the clicked item disappointing. What's even worse, optimizing CTR models on such flawed data will result in the Matthew Effect, making the seemingly attractive but actually low-quality items be more frequently recommended. In this paper, we formulate the recommendation models as a causal graph that reflects the cause-effect factors in recommendation, and address the clickbait issue by performing counterfactual inference on the causal graph. We imagine a counterfactual world where each item has only exposure features (i.e., the features that the user can see before making a click decision). By estimating the click likelihood of a user in the counterfactual world, we are able to reduce the direct effect of exposure features and eliminate the clickbait issue. Experiments on real-world datasets demonstrate that our method significantly improves the post-click satisfaction of CTR models.