Reliable Content Recommendations with Counterfactual Learning

Entire Space Counterfactual Learning for Reliable Content Recommendations

Source: arXiv:2210.11039v3

Type: replace-cross

Abstract

Post-click conversion rate (CVR) estimation is a fundamental task in developing effective recommender systems, yet it faces challenges from data sparsity and sample selection bias. To handle both challenges, the entire space multitask models are employed to decompose the user behavior track into a sequence of exposure, click, and conversion, constructing surrogate learning tasks for CVR estimation. However, these methods suffer from two significant defects:

• Intrinsic Estimation Bias (IEB): This defect leads to CVR estimates that are higher than the actual values.
• False Independence Prior (FIP): This issue arises when the causal relationship between clicks and subsequent conversions is potentially overlooked.

Introduction

In the realm of recommender systems, accurately estimating the post-click conversion rate (CVR) is crucial for optimizing user engagement and driving revenue. Traditional methods often struggle with the issues of data sparsity and sample selection bias, which can skew the accuracy of CVR predictions. Addressing these problems requires innovative approaches that enhance the reliability of content recommendations.

Methodology

To tackle the challenges associated with CVR estimation, researchers have developed the Entire Space Counterfactual Multitask Model (ESCM²). This model-agnostic framework builds upon the existing Entire Space Multitask Model (ESMM), incorporating a counterfactual risk minimizer to regularize CVR estimation effectively.

The ESCM² framework aims to decompose user behavior into a systematic sequence of events: exposure leading to a click, followed by conversion. This structured approach allows for the construction of surrogate learning tasks that help mitigate the issues of intrinsic estimation bias and false independence prior.

Key Innovations

ESCM² introduces several key innovations:

• Counterfactual Risk Minimization: This technique adjusts the CVR estimates by considering what the outcomes would have been under different circumstances, thereby reducing intrinsic estimation bias.
• Interconnected Learning Tasks: By recognizing the causal relationships between clicks and conversions, ESCM² ensures that the learning tasks are interrelated, addressing the false independence prior.

Experimental Results

Extensive experiments conducted on large-scale industrial recommendation datasets and an online industrial recommendation service demonstrate the effectiveness of the ESCM² model. The results indicate a significant reduction in both intrinsic estimation bias and false independence prior, leading to a marked improvement in overall recommendation performance.

Conclusion

The Entire Space Counterfactual Multitask Model represents a promising advancement in CVR estimation for recommender systems. By addressing the fundamental challenges of data sparsity and sample selection bias, ESCM² not only enhances the reliability of content recommendations but also paves the way for future research in this critical area of artificial intelligence.

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