Triple-Feature Transformer with Sparsity Regularization

The RecSys Challenge 2025 focuses on developing robust recommendation systems capable of generalizing across multiple tasks in a highly sparse and dynamic user-behavior dataset. Designing unified user representations that generalize across multiple recommendation tasks under extreme sparsity remains a key challenge. In this paper, we present TFT-SR (Triple-Feature Transformer with Sparsity Regularization), a unified framework for behavioral modeling in the RecSys Challenge 2025. Our method fuses three complementary types of features-statistical descriptors, quantized temporal patterns, and hashed high-cardinality IDs-into a unified user vector. A dual-path neural encoder is used to separately extract dense and sparse representations, with the sparse branch regularized by an L1 penalty to promote interpretability and efficiency. Multi-task optimization [13, 15, 18] is performed through loss weighting, ensuring balanced learning across tasks. we participated in the competition under the team name 'xunzhou,' achieving 11th place on the final leaderboard and 5th place on the academic leaderboard.