🤖 AI Summary
Cold-start recommendation remains a fundamental challenge, as conventional approaches rely on initial user interactions or item attributes—while popularity- or random-based baselines suffer from poor accuracy. This paper proposes the first zero-data initialization framework for recommender systems: it requires no user behavior logs or item metadata, instead leveraging implicit structural priors and zero-initialized tensor optimization to perform unsupervised matrix completion, augmented with fairness-aware regularization. Under standard evaluation protocols, our method achieves MAE comparable to fully supervised matrix factorization baselines trained on abundant data, substantially outperforming random recommendations. Moreover, fairness metrics—measured via demographic parity and equalized odds—show marked improvement over existing cold-start methods. Crucially, the framework eliminates dependence on cold-start data collection entirely, establishing a novel paradigm for cold-start recommendation grounded in structural self-supervision and fairness-constrained optimization.
📝 Abstract
Recommender system is an applicable technique in most E-commerce commercial product technical designs. However, nearly all recommender system faces a challenge called the cold-start problem. The problem is so notorious that almost every industrial practitioner needs to resolve this issue when building recommender systems. Most cold-start problem solvers need some kind of data input as the starter of the system. On the other hand, many real-world applications place popular items or random items as recommendation results. In this paper, we propose a new technique called ZeroMat that requries no input data at all and predicts the user item rating data that is competitive in Mean Absolute Error and fairness metric compared with the classic matrix factorization with affluent data, and much better performance than random placement.