arXiv:2607. 15590v1 Announce Type: cross Abstract: This paper presents our solution to the KDD Cup 2026 Tencent UNIREC Challenge.
Summary generated by The Flow from the publisher's feed. 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:2607. 11030v1 Announce Type: cross Abstract: Multimodal information is pivotal for e-commerce search ranking.
arXiv:2607. 17017v1 Announce Type: cross Abstract: As scalability becomes increasingly important in recommendation modeling, recent architectures have advanced the modeling of two broad sources of ranking signals along separate paths: non-sequence features, including user, item, context, and cross features; and sequence features from user behavior histories.
arXiv:2607. 28659v1 Announce Type: new Abstract: Cross-domain sequential recommendation (CDSR) aims to model users' dynamic interest transitions and sequential patterns across multiple domains.