arXiv:2607. 15590v1 Announce Type: cross Abstract: This paper presents our solution to the KDD Cup 2026 Tencent UNIREC Challenge.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2607. 22700v1 Announce Type: new Abstract: Post-click conversion rate (PCVR) prediction is central to industrial recommendation, but remains challenged by the structural mismatch between sparse, unordered multi-field features and long, domain-specific behavior histories.
arXiv:2606. 25147v1 Announce Type: cross Abstract: User modeling in industrial recommender systems typically produces dense embeddings, which suffer from representational constraints inherent to fixed-dimensional vectors.
arXiv:2607. 10016v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as backbone architectures for recommender systems because of their strong sequence modeling and representation learning capabilities.
arXiv:2603.02561v2 Announce Type: replace-cross Abstract: Attention mechanism remains the defining operator in Transformers since it provides expressive global credit assignment, yet its quadratic co...
The paper introduces CRAFT, a Contextual Residual Adaptive Feature Transport block that treats unified recommendation models as a discrete context‑conditioned representation evolution process. By summarizing non‑sequential features into a reliability‑aware contextual field, CRAFT generates residual displacement and memory‑preserving signals to control intent and sequence representations. Experiments on the TAAC2026 competition show CRAFT achieving a test AUC of 0.838090, surpassing the previous best, and further improvements with deeper or wider models.
arXiv:2607. 11030v1 Announce Type: cross Abstract: Multimodal information is pivotal for e-commerce search ranking.