Modeling and Mitigating Selection Bias under Neighborhood Effect in Recommender Systems

Core Concepts

The core message of this paper is to formally formulate the neighborhood effect as an interference problem in causal inference, introduce a treatment representation to capture the neighborhood effect, and propose novel unbiased estimators to eliminate selection bias in the presence of neighborhood effect.

Abstract

The paper addresses the problem of selection bias in recommender systems, which arises from the recommendation process and the interactive process of user selection. Many previous studies have focused on addressing selection bias by establishing unbiased estimators, but they ignore the fact that potential outcomes for a given user-item pair may vary with the treatments assigned to other user-item pairs, known as the neighborhood effect.
To fill this gap, the paper first formulates the neighborhood effect as an interference problem from the perspective of causal inference. It then introduces a treatment representation to capture the neighborhood effect and proposes a novel ideal loss that can be used to deal with selection bias in the presence of neighborhood effect.
The paper further develops two new estimators, neighborhood inverse propensity score (N-IPS) and neighborhood doubly robust (N-DR), for estimating the proposed ideal loss. Theoretical analysis shows that the proposed N-IPS and N-DR estimators can achieve unbiased learning in the presence of both selection bias and neighborhood effect, while the previous debiasing estimators cannot result in unbiased learning without imposing extra strong assumptions.
Extensive semi-synthetic and real-world experiments are conducted to demonstrate the effectiveness of the proposed methods for eliminating the selection bias under interference.

Stats

The paper uses the MovieLens 100K dataset, which contains 100,000 missing-not-at-random (MNAR) ratings from 943 users to 1,682 movies.

Quotes

"Selection bias is widespread in recommender system (RS) and challenges the prediction of users' true preferences (Wu et al., 2022; Chen et al., 2023), which arises from the recommendation process of system filtering and the interactive process of user selection (Marlin and Zemel, 2009; Huang et al., 2022)."
"However, the theoretical guarantees of the previous methods are all established under the Stable Unit Treatment Values Assumption (SUTVA) (Rubin, 1980), which requires that the potential outcomes for one user-item pair do not vary with the treatments assigned to other user-item pairs (also known as no interference or no neighborhood effect), as shown in Figure 1(a). In fact, such an assumption can hardly be satisfied in real-world scenarios."

Key Insights Distilled From

Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference

by Haoxuan Li,C... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19620.pdf

Deeper Inquiries

How can the proposed methods be extended to handle more complex neighborhood structures, such as hierarchical or dynamic neighborhoods

To extend the proposed methods to handle more complex neighborhood structures, such as hierarchical or dynamic neighborhoods, several modifications and enhancements can be considered.

Hierarchical Neighborhoods:

Introduce a multi-level treatment representation approach that can capture hierarchical relationships within the neighborhood structure. This can involve encoding different levels of neighbors and their interactions to provide a more comprehensive representation of the neighborhood effect.
Develop a mechanism to dynamically adjust the treatment representation based on the hierarchical relationships within the neighborhood. This adaptive approach can help in capturing the varying influences of different levels of neighbors on the target user-item pair.

Dynamic Neighborhoods:

Implement a dynamic treatment representation that can adapt to changes in the neighborhood structure over time. This can involve incorporating temporal information to capture the evolving relationships between users and items in the neighborhood.
Utilize reinforcement learning techniques to learn the optimal treatment representation in real-time, considering the dynamic nature of the neighborhood effect. This adaptive learning approach can enhance the model's ability to handle changing neighborhood dynamics.

By incorporating these strategies, the proposed methods can be extended to effectively handle more complex neighborhood structures, providing a more accurate representation of the neighborhood effect in recommender systems.

What are the potential limitations of the current treatment representation approach, and how can it be further improved to capture more nuanced neighborhood effects

The current treatment representation approach may have some potential limitations that could be addressed for further improvement:

Limited Expressiveness:

The current treatment representation may have limitations in capturing the full complexity of neighborhood effects, especially in scenarios with intricate relationships among users and items. To address this, more sophisticated representation learning techniques, such as graph neural networks, can be explored to capture nuanced neighborhood structures.

Scalability:

The scalability of the treatment representation approach may be a concern when dealing with large-scale datasets with extensive neighborhood interactions. Implementing scalable algorithms and efficient data structures can help improve the scalability of the treatment representation method.

Interpretability:

The interpretability of the treatment representation may be challenging, making it difficult to understand how different features contribute to capturing the neighborhood effect. Incorporating explainable AI techniques or feature importance analysis can enhance the interpretability of the treatment representation.

By addressing these limitations and further refining the treatment representation approach, the model can better capture and utilize the nuanced neighborhood effects in recommender systems.

How can the proposed methods be integrated with other debiasing techniques, such as data augmentation or adversarial training, to achieve even better performance in real-world recommender systems

Integrating the proposed methods with other debiasing techniques, such as data augmentation or adversarial training, can enhance the performance of the model in real-world recommender systems:

Data Augmentation:

Data augmentation techniques can be used to generate synthetic data points that reflect diverse neighborhood interactions. By augmenting the training data with variations of user-item pairs and their corresponding treatments, the model can learn to generalize better to different neighborhood structures.

Adversarial Training:

Adversarial training can be employed to enhance the robustness of the model against potential biases in the neighborhood effect. By training the model to resist adversarial attacks that aim to introduce biases in the neighborhood representation, the model can learn more robust and unbiased representations.

Ensemble Approaches:

Combining the proposed methods with ensemble techniques, such as model averaging or stacking, can further improve the model's performance. By leveraging the diversity of multiple debiasing methods, the ensemble model can capture a broader range of neighborhood effects and enhance the overall recommendation accuracy.

By integrating these complementary debiasing techniques with the proposed methods, recommender systems can achieve more robust and accurate performance in real-world scenarios.