Introduction
Recommendation systems have become integral to modern digital platforms, helping users discover content, products, and services tailored to their preferences. However, despite their widespread adoption, these systems often suffer from biases that skew recommendations toward popular items while neglecting long-tail or niche content. This phenomenon not only reduces diversity but also diminishes user satisfaction over time. Traditional debiasing approaches, such as inverse propensity scoring (IPS), causal inference, and regularization, typically address only a single type of bias, making them ineffective in real-world scenarios where multiple biases coexist.
To overcome these limitations, recent research has explored disentangled representation learning (DRL), which separates user-item interactions into intrinsic preferences and external biases. While promising, existing DRL-based methods treat negative samples as a homogeneous group, failing to account for the nuanced relationships between users and both positive and negative samples. Additionally, the imbalanced representation of popular and long-tail items exacerbates data sparsity, further distorting model performance.
This article introduces a novel debiasing recommendation method called DTDN (Debiasing Recommendation Method Integrating Transfer Learning and Disentangled Negative Sampling), which addresses these challenges through a multi-faceted approach. DTDN leverages causal inference to design a negative sampling strategy, employs disentangled representation learning to separate intrinsic and biased features, and incorporates transfer learning to align the distributions of popular and long-tail items. The method also introduces a sample selector to refine training data, ensuring the model learns accurate user preferences.
Background and Motivation
Challenges in Recommendation Systems
Modern recommendation systems rely heavily on historical interaction data, which often contains inherent biases. For example, users may interact with popular items due to social influence rather than genuine interest, leading to skewed training data. Additionally, long-tail items—those with fewer interactions—are frequently underrepresented, making it difficult for models to learn their true relevance. These biases create a feedback loop where popular items receive even more exposure, while high-quality but less popular items remain undiscovered.
Existing debiasing techniques fall into three main categories:
- Prior Knowledge-Based Methods: These approaches target specific biases, such as popularity bias, by reweighting items or using causal inference. However, they struggle with multiple or unknown biases.
- Unbiased Data Augmentation: Some methods use unbiased datasets to guide model training, but obtaining such datasets is costly and impractical.
- Disentangled Representation Learning (DRL): These techniques separate intrinsic user preferences from external biases but often fail to properly model negative samples, leading to suboptimal performance.
The Need for Negative Sample Modeling
Negative samples—items users have not interacted with—play a crucial role in training recommendation models. However, treating all negative samples uniformly ignores critical distinctions:
- Some negative samples may actually align with user preferences but were simply undiscovered.
- The impact of biases on negative samples varies over time.
- Long-tail items suffer from sparse interactions, making their representation learning particularly challenging.
DTDN addresses these issues by introducing a disentangled negative sampling strategy, ensuring that both positive and negative samples are properly modeled.
Methodology
Overview of DTDN
DTDN consists of four key components:
- Negative Sampling Module: Selects negative samples based on popularity differences to capture bias-related patterns.
- Feature Disentanglement Module: Separates intrinsic and biased features of users and items.
- Transfer Learning Module: Aligns the representations of popular and long-tail items to mitigate sparsity.
- Sample Selector Module: Filters training data to remove inherent biases, improving model accuracy.
Negative Sampling Strategy
Traditional negative sampling randomly selects unobserved items, but this approach fails to account for bias-related patterns. DTDN instead uses collider bias—a causal concept where two independent factors become correlated due to a shared effect—to guide sampling.
The method categorizes negative samples into two datasets:
- D₁: Contains negative samples with significantly lower popularity than positive samples.
- D₂: Contains negative samples with significantly higher popularity than positive samples.
By analyzing these datasets, DTDN learns to distinguish between:
- Intrinsic preferences (why users interact with certain items).
- External biases (why users avoid certain items due to popularity or other factors).
Disentangled Representation Learning
To separate intrinsic and biased features, DTDN employs two independent encoders:
- Intrinsic Feature Encoder: Captures genuine user preferences.
- Bias Feature Encoder: Models external influences like popularity.
A contrastive learning mechanism ensures that these representations remain distinct, preventing re-entanglement during training.
Transfer Learning for Long-Tail Items
Long-tail items suffer from sparse interactions, making their representations unreliable. DTDN introduces a Maximum Mean Discrepancy (MMD)-based transfer learning module to align the distributions of popular and long-tail items. This alignment ensures that long-tail items receive adequate representation, improving recommendation diversity.
Sample Selector for Bias Removal
The final component, the sample selector, filters training data to retain only high-quality samples:
- Removes positive samples that users interacted with due to bias (e.g., popularity).
- Selects negative samples that users genuinely dislike.
This refined dataset ensures that the model learns true user preferences rather than biased patterns.
Experimental Evaluation
Datasets and Metrics
DTDN was evaluated on two real-world datasets:
- MovieLens-10M: A movie recommendation dataset with significant popularity bias.
- Netflix: A video streaming dataset with a long-tail distribution of items.
Performance was measured using:
- Hit Rate (HR@K): Whether recommended items include user-preferred content.
- Recall@K: The proportion of preferred items captured in recommendations.
- Normalized Discounted Cumulative Gain (NDCG@K): Measures ranking quality with position-based discounts.
Comparative Results
DTDN was compared against several state-of-the-art debiasing methods, including:
- BMSE: Adjusts item weights based on popularity.
- CausPref: Uses causal inference for debiasing.
- DICE: A disentangled representation learning approach.
- CD²AN: Aligns item popularity distributions.
Results showed that DTDN consistently outperformed baselines across all metrics, demonstrating its effectiveness in mitigating bias.
Ablation Studies
Ablation experiments confirmed the contributions of each DTDN component:
- Removing the negative sampling module led to a significant performance drop, highlighting its importance in capturing bias patterns.
- Disabling transfer learning reduced performance on long-tail items, confirming its role in representation alignment.
- Excluding the sample selector resulted in biased recommendations, emphasizing its necessity for refining training data.
Case Study
A real-world test case compared DTDN with BMSE and CD²AN for two users with distinct preferences:
- User A: Preferred deep, artistic films.
- User B: Favored mainstream action movies.
DTDN provided more personalized recommendations, successfully balancing popular and niche items, while other methods either overemphasized popularity or failed to capture user preferences accurately.
Conclusion
DTDN presents a robust solution to the persistent challenge of bias in recommendation systems. By integrating disentangled negative sampling, transfer learning, and sample selection, it effectively separates intrinsic user preferences from external biases while ensuring fair representation for long-tail items. Experimental results demonstrate its superiority over existing methods, making it a promising approach for real-world deployment.
Future research could explore leveraging biases constructively—for instance, by dynamically adjusting recommendations based on evolving trends while maintaining fairness. Additionally, extending DTDN to sequential and multi-modal recommendation scenarios could further enhance its applicability.
DOI: 10.19734/j.issn.1001-3695.2024.09.0296
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