Self-supervised Contrastive Learning for Implicit Collaborative Filtering: Addressing Pseudo-Positive and Pseudo-Negative Examples

Kernekoncepter

The author proposes a self-supervised contrastive learning framework to address issues with pseudo-positive and pseudo-negative examples in collaborative filtering, aiming to improve preference learning accuracy.

Resumé

Self-supervised recommendation algorithms have significantly advanced the field by leveraging SSL techniques. The proposed method enhances user-item feature representations by addressing false-positive and false-negative examples through positive feature augmentation and negative label augmentation. The theoretical analysis provides insights into maximizing likelihood estimation with latent variables representing user interest centers. Experimental results show significant improvements in top-k ranking accuracy across multiple datasets.

Statistik

The density of MovieLens-100k dataset is 0.06304.
In Yelp2018, there are 1,561,406 interactions.
HCL introduces hard negative mining with a parameter beta for up-weighting difficult negatives.
The probability of an item being labeled as a negative example is proportional to its relative ranking position when alpha is greater than 0.5.

Citater

"Contrastive learning-based recommendation algorithms have significantly advanced the field of self-supervised recommendation."
"Our method maintains strict linear complexity relative to BPR while achieving significant improvements in top-k ranking accuracy."

Vigtigste indsigter udtrukket fra

Self-supervised Contrastive Learning for Implicit Collaborative Filtering

by Shipeng Song... kl. arxiv.org 03-13-2024

https://arxiv.org/pdf/2403.07265.pdf

Dybere Forespørgsler

How can the proposed self-supervised contrastive learning framework be applied to other recommendation systems beyond collaborative filtering

The proposed self-supervised contrastive learning framework can be applied to various recommendation systems beyond collaborative filtering by adapting the concept of interest centers and negative label augmentation. One potential application is in content-based recommendation systems, where user preferences are inferred based on item features rather than user-item interactions. In this scenario, the interest center for a user could represent a cluster of items that best align with their preferences, allowing for more accurate recommendations. Additionally, the negative label augmentation technique could help address challenges related to noisy or incomplete data in content-based systems by identifying items that users do not prefer.
Another application area could be in hybrid recommendation systems that combine collaborative and content-based approaches. By incorporating the self-supervised contrastive learning framework into such hybrid models, it becomes possible to leverage both user-item interactions and item features effectively. The interest centers derived from positive examples can enhance the understanding of user preferences across different types of items, leading to more personalized recommendations.
Furthermore, this framework could also be extended to sequential recommendation tasks where the order of interactions matters. By considering sequences of positive examples as input for feature augmentation and dynamically adjusting negative labels based on context or temporal information, the model can capture evolving user preferences over time.

What potential challenges or criticisms could arise from relying on pseudo-labels for preference comparison in self-supervised settings

Relying on pseudo-labels for preference comparison in self-supervised settings may introduce several challenges and criticisms:

Label Noise: Pseudo-labels generated based on implicit feedback may not always accurately reflect true user preferences due to factors like noise in interaction data or lack of explicit ratings. This can lead to suboptimal training signals and impact model performance.

Generalization Issues: Models trained using pseudo-labels may struggle with generalizing well to unseen scenarios or diverse user behaviors since they are optimized based on limited information captured through implicit feedback.

Bias Amplification: If there are inherent biases present in the training data used for generating pseudo-labels (e.g., popularity bias), relying solely on these labels can amplify existing biases in recommendations made by the system.

Scalability Concerns: As datasets grow larger or more complex, managing pseudo-label generation processes efficiently while maintaining label quality becomes increasingly challenging.

How might the concept of interest centers for users impact the scalability and generalizability of the model in real-world applications

The concept of interest centers for users has implications for scalability and generalizability in real-world applications:

Scalability: Implementing interest centers requires additional computational resources compared to traditional methods due to calculating representative positive samples' embeddings during training iterations.

2 .Generalizability: While interest centers aim at capturing overall patterns within a set of positive examples per user rather than individual instances' specifics—this approach might improve generalization capabilities by focusing on broader trends instead.
3 .Data Efficiency: Interest centers have potential benefits when dealing with sparse datasets as they provide a consolidated representation reflecting multiple similar positive instances—aids efficient use even when explicit preference labels are scarce.
4 .Interpretability & Adaptation: Understanding users' interests through these central representations offers interpretability insights into why certain recommendations were made—enabling adaptive strategies aligned with evolving needs/preferences over time.