ברכה שפירא

Understanding long-term user preferences is critical for delivering consistent and personalized recommendations that go beyond short-term behavioral cues in large-scale e-commerce platforms. We present NILUS (Neural Inference for Long-Term User Signals), a content-based transformer model trained to predict user behavior over a K-day future window using up to one year of historical interaction data. NILUS learns user embeddings end-to-end via contrastive learning, using item representations from a fine-tuned sentence encoder. We introduce a novel evaluation framework to assess the model's ability to capture enduring user interests, and demonstrate that NILUS delivers higher accuracy than strong baselines on a large-scale offline dataset spanning millions of users and diverse product verticals. When combined with short-term signals, NILUS further improves recommendation accuracy and diversity. Finally, a large-scale online A/B test on a multinational e-commerce platform confirms statistically significant gains in user engagement.