Time-Period-Aware Embedding Regeneration for Session-Based Recommendation

Session-based recommender systems typically focus on intra-session user behavior but often overlook the macro-level temporal evolution of items themselves. To address this gap, we introduce a model that explicitly captures item dynamics by regenerating time-period-aware embeddings. Our approach partitions the data timeline into several distinct periods and employs a simple yet effective module, TEG, which uses a GRU and a causal attention layer to recurrently learn how item representations evolve from one period to the next. This allows our model to capture global, long-term trends while its gating mechanism naturally accommodates both dynamic and static items. Extensive experiments on three real-world datasets show that our method achieves highly competitive performance against complex state-of-the-art models. More importantly, it demonstrates the significant and complementary value of modeling global item evolution, providing a new dimension for improving session-based recommendation.