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UniDot是统一推荐系统中特征交互与序列建模的架构，经特定优化方法训练后，在TAAC KDD Cup 2026工业赛道获亚军。
AI 中文摘要
工业推荐系统依赖两类演化过程中基本独立的模型族：一类是针对多字段用户/物品特征的特征交互模型，另一类是针对用户行为历史的序列模型。生产系统仅将二者松散耦合。为统一这两类模型，我们提出UniDot，一种从因子分解机（FM）角度构建的新型后点击转化预测架构：支撑协同过滤、使推荐系统能泛化到未见用户-物品对的嵌入内积，与注意力的查询-点积-键评分是同一基础原语，因此单个标记的点积可同时作为特征交互与序列建模的基础。UniDot将非序列字段和多域行为序列标记化为同一共享标记空间，并堆叠单个宏块，其中标记混合总线与序列检索总线（物品标记跨注意力关注历史）并行运行，每一层通过MLP-Mixer融合交换状态，同时FM Highway将显式的每层点积交互沿残差栈直接传递至分类器。序列侧每前向传播嵌入一次，被所有模块共享，从而限制推理延迟。UniDot采用双稀疏/密集优化器（Adagrad + Muon）、辅助转化延迟头和多路径互学习训练，在TAAC KDD Cup 2026工业赛道中获得亚军。
英文摘要
Industrial recommenders rely on two model families that have evolved largely independently: feature-interaction models over multi-field user/item features, and sequential models over user-behavior histories. Production systems couple them only loosely. To unify the two, we present UniDot, a novel architecture for post-click conversion prediction built from the factorization-machine (FM) point of view: the embedding inner product---which powers collaborative filtering and lets a recommender generalize to unseen user--item pairs---is the same primitive as attention's query dot key scoring, so a single dot-product of tokens can underlie both feature interaction and sequence modeling. UniDot tokenizes non-sequential fields and multi-domain behavioral sequences into one shared token space and stacks a single macro-block in which a token-mixing bus and a sequence-retrieval bus (item tokens cross-attending the histories) run in parallel and exchange state each layer through an MLP-Mixer fusion, while an FM Highway carries explicit per-layer dot-product interactions around the residual stack directly to the classifier. The sequence side is embedded once per forward pass and shared by all consumers, bounding inference latency. Trained with a dual sparse/dense (Adagrad + Muon) optimizer, an auxiliary conversion-delay head, and multi-path mutual learning, UniDot finished as the runner-up on the Industrial track of the TAAC KDD Cup 2026.
Journal refKDD 2026 UniRec Workshop
