Mitigating Bias in Recommender Systems by Separating and Learning Latent Confounders

Concepts de base

The core message of this paper is to mitigate the influence of unmeasured confounders and former recommender systems on user preferences by separating the confounders and user preferences in the latent parameter space, and using the representation of the confounders to guide the model in capturing the true preferences of users.

Résumé

The paper investigates the problem of debiasing in recommender systems when incorporating the effects of former recommender systems and unmeasured confounders. It states the assumption of independence of confounders and user preferences, which is the basis for separating them in the latent parameter space.
The paper proposes a novel framework, Separating and Learning Latent Confounders For Recommendation (SLFR), which obtains the representation of latent confounders to identify the counterfactual feedback by disentangling user preferences and latent confounders, then guides the target model to capture the true preferences of users.
The key steps of SLFR are:

Learning representation of unmeasured confounders: Pre-train two VAE models to learn the representations of confounders that are independent of user and item, respectively.
True Preference Modeling: Calculate the relevance score considering the influence of confounders, and use it to obtain the counterfactual preference in the absence of confounders as the true preference.

Extensive experiments on five real-world datasets validate the advantages of the proposed SLFR framework, demonstrating its effectiveness in mitigating the influence of confounders and former recommender systems to capture the true user preferences.

Stats

The percentage of positive feedback is much higher in the Reasoner dataset than in the usual dataset, implying that the feedback-true preference false matching problem due to the presence of the former recommender system is even more severe in the usual datasets.

Citations

"The feedback data generated by the interaction between the user and the suboptimal item list can not reflect the true preferences of the user, and recommender systems trained on these data further exacerbate the preference bias."
"The causes of the influence of recommender systems on user preferences are usually user-independent (e.g., recommendation policies), and most of the unmeasured confounders are independent of the user, which provides the necessary premise for further separation of user preferences and confounders."

Idées clés tirées de

Separating and Learning Latent Confounders to Enhancing User Preferences Modeling

by Hangtong Xu,... à arxiv.org 04-03-2024

https://arxiv.org/pdf/2311.03381.pdf

Questions plus approfondies

How can the proposed framework be extended to handle dynamic changes in user preferences and confounders over time

To handle dynamic changes in user preferences and confounders over time, the proposed framework can be extended by incorporating a mechanism for continuous learning and adaptation. This can involve implementing a system that regularly updates the representations of confounders and user preferences based on new data. By utilizing techniques like online learning or incremental updates, the framework can adjust to evolving user behaviors and changing confounding factors. Additionally, incorporating feedback loops that monitor model performance and trigger retraining when significant shifts are detected can help ensure the framework remains effective in capturing the most current user preferences and confounders.

What are the potential limitations of the independence assumption between confounders and user preferences, and how can this assumption be relaxed in future work

The independence assumption between confounders and user preferences may have limitations in real-world scenarios where the relationship between these factors is more complex and intertwined. One potential limitation is that certain confounders may have a direct impact on user preferences, leading to dependencies that violate the independence assumption. To relax this assumption, future work could explore more sophisticated modeling techniques that allow for interactions between confounders and user preferences. This could involve incorporating interaction terms or latent variables that capture the joint influence of confounders and user preferences, enabling a more nuanced understanding of their relationship. Additionally, employing causal inference methods that can handle non-linear relationships and feedback loops may help relax the independence assumption and provide a more accurate representation of the underlying dynamics.

How can the insights from this work on separating confounders and user preferences be applied to other domains beyond recommender systems, such as personalized decision-making or causal inference in social sciences

The insights gained from separating confounders and user preferences in recommender systems can be applied to other domains such as personalized decision-making and causal inference in social sciences. In personalized decision-making, understanding and disentangling the factors that influence individual choices can lead to more effective recommendation systems and tailored interventions. By identifying and isolating confounding variables that impact decision-making processes, personalized systems can provide more accurate and relevant suggestions to users. Similarly, in social sciences, the ability to separate confounders from user preferences can enhance causal inference studies by enabling researchers to better control for external factors that may influence outcomes. This can lead to more robust and reliable conclusions in studies examining the effects of interventions or policies on human behavior and societal outcomes.