Learning Behaviorally Grounded Item Embeddings via Personalized Temporal Contexts
Effective user modeling requires distinguishing between short-term and long-term preference evolution. While item embeddings have become a key component of recommender systems, standard approaches like Item2Vec treat user histories as unordered sets (bag-of-items), implicitly assuming that interactions separated by minutes are as semantically related as those separated by months. This simplification flattens the rich temporal structure of user behavior, obscuring the distinction between coherent consumption sessions and gradual interest drifts. In this work, we introduce TAI2Vec (Time-Aware Item-to-Vector), a family of lightweight embedding models that integrates temporal proximity direct...
| Authors | Rafael T. Sereicikas, Pedro R. Pires, Gregorio F. Azevedo et al. |
|---|---|
| Primary category | cs.IR |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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