Unlocking the Unlabeled Data: Enhancing Recommendations with Neutral Samples and Uncertainty

Collaborative filtering (CF) is a fundamental technique in recommender systems, yet utilizing the vast amount of unlabeled data effectively poses a significant challenge. Recent research endeavors have concentrated on extracting subsets of this data that approximate negative samples. Regrettably, the remaining data are overlooked, failing to fully integrate this valuable information into the construction of user preferences. To bridge this gap, we propose a novel positive-neutral-negative learning paradigm (PNNP). PNNP introduces a neutral class that includes complex items that are difficult to categorize directly as positive or negative. By training a model based on this triple-wise partial ranking, PNNP provides a promising avenue for learning intricate user preferences. Through theoretical analysis, we connect PNNP to the one-way partial AUC (OPAUC) to validate its effectiveness. Implementing the PNN paradigm is, however, technically challenging because: (1) Modeling Neutral Samples: Users’ attitudes towards items classified as neutral can be complex and uncertain, requiring advanced modeling techniques. (2) Classifying Unlabeled Data: Without supervised signals, distinguishing between neutral and negative samples within unlabeled data is particularly challenging. (3) Lack of Suitable Loss Functions: There is no existing loss function that effectively manages set-level triple-wise ranking relationships.
To address these challenges, we propose an innovative method to model neutral samples through the lens of uncertainty. Instead of representing neutral samples as fixed points in a high-dimensional space, we use Elliptical Gaussian Distributions to encapsulate their inherent uncertainty effectively. We then introduce a semi-supervised learning method combined with a user-aware attention model for enhanced knowledge acquisition and classification refinement. Furthermore, a novel loss function with a two-step centroid ranking approach is developed to handle set-level rankings. Extensive experiments on four real-world datasets demonstrate that, when integrated with PNNP, a broad range of representative CF models can consistently and significantly enhance their performance. Even a simple matrix factorization model, when combined with PNNP, can achieve performance comparable to sophisticated graph neural networks. Our code is publicly available at https://github.com/Asa9aoTK/PNN-RecBole.