Addressing Mainstream Bias in Recommender Systems through End-to-End Adaptive Local Learning

Grunnleggende konsepter

The proposed End-to-End Adaptive Local Learning (TALL) framework effectively counters mainstream bias in recommender systems by addressing the discrepancy modeling problem and the unsynchronized learning problem.

Sammendrag

The paper identifies two core root causes of mainstream bias in recommender systems: the discrepancy modeling problem and the unsynchronized learning problem. To address these issues, the authors propose the End-to-End Adaptive Local Learning (TALL) framework.

To tackle the discrepancy modeling problem, TALL integrates a loss-driven Mixture-of-Experts module that adaptively provides customized models for different users through an end-to-end learning procedure. The adaptive loss-driven gate module assigns high gate values to expert models that are effective for the target user and low values to less effective expert models.

To address the unsynchronized learning problem, TALL involves an adaptive weight module that dynamically adjusts weights in the objective function to synchronize the learning paces of different users. The adaptive weight module uses a loss change mechanism and a gap mechanism to make the weight computation more robust and stable.

Extensive experiments on three datasets demonstrate that TALL significantly outperforms state-of-the-art debiasing methods, enhancing utility for niche users by 6.1% over the best baseline with equal model complexity. The ablation study validates the effectiveness of the key components in the TALL framework.

Statistikk

The mainstream score for a user u is calculated as MSu = Σv∈U\u Sim(Ou, Ov)/(N-1), where Sim(Ou, Ov) is the Jaccard similarity between the implicit feedback records of users u and v.
Users are divided into five subgroups of equal size based on their mainstream scores: 'low', 'med-low', 'medium', 'med-high', and 'high'.

Sitater

"Collaborative filtering (CF) based recommendations suffer from mainstream bias – where mainstream users are favored over niche users, leading to poor recommendation quality for many long-tail users."
"We identify two root causes of this mainstream bias: (i) discrepancy modeling, whereby CF algorithms focus on modeling mainstream users while neglecting niche users with unique preferences; and (ii) unsynchronized learning, where niche users require more training epochs than mainstream users to reach peak performance."

Viktige innsikter hentet fra

Countering Mainstream Bias via End-to-End Adaptive Local Learning

by Jinhao Pan,Z... klokken arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.08887.pdf

Dypere Spørsmål

How can the proposed TALL framework be extended to handle other types of recommendation biases beyond mainstream bias, such as popularity bias or exposure bias?

The TALL framework can be extended to address other types of recommendation biases by incorporating specific modules tailored to each type of bias. For example, to tackle popularity bias, the framework can include a module that adjusts the recommendations based on the popularity of items. This module can dynamically adjust the weights assigned to popular items to ensure that less popular items are also recommended to users. Similarly, for exposure bias, a module can be designed to control the exposure of certain items to users, ensuring a more balanced recommendation strategy.
By incorporating these additional modules into the TALL framework, it can adapt to different types of biases and provide more personalized and fair recommendations to users. The key lies in identifying the specific characteristics of each bias and designing adaptive mechanisms within the framework to mitigate their effects effectively.

How can the proposed TALL framework be extended to handle other types of recommendation biases beyond mainstream bias, such as popularity bias or exposure bias?

The adaptive weight module in TALL may have limitations in scenarios where the loss function is not a reliable indicator of learning progress or where the scale of the loss varies significantly across different user groups. To address these limitations, the adaptive weight module can be further improved by incorporating additional metrics or indicators of learning progress. For example, instead of solely relying on the loss function, metrics like convergence rate or prediction accuracy improvement could be considered in determining the weights for different users.
Furthermore, to better synchronize the learning paces of different user groups, the adaptive weight module can be enhanced by introducing more sophisticated algorithms for weight adjustment. Techniques such as reinforcement learning or meta-learning can be explored to dynamically adapt the weights based on the learning progress of individual users. Additionally, incorporating feedback mechanisms to adjust the weights iteratively during the training process can help fine-tune the synchronization of learning paces more effectively.

Given the end-to-end nature of TALL, how can the framework be adapted to incorporate additional user or item features beyond the implicit feedback data to enhance the debiasing performance?

To incorporate additional user or item features beyond the implicit feedback data in the TALL framework, the model architecture can be extended to include multi-modal inputs. By integrating diverse data sources such as user demographics, item attributes, or contextual information, the framework can capture a more comprehensive representation of user preferences and item characteristics.
One approach to incorporating additional features is through feature fusion techniques, where the different modalities of data are combined at various stages of the model. For instance, user embeddings learned from demographic data can be concatenated with the user embeddings derived from implicit feedback data to create a more enriched user representation. Similarly, item embeddings can be augmented with features such as item category, price, or popularity to enhance the item representation.
Moreover, the TALL framework can leverage attention mechanisms to dynamically weigh the importance of different features during the recommendation process. By attending to relevant user and item features based on the context of the recommendation task, the model can adaptively incorporate additional information to improve the debiasing performance and recommendation quality.