Scaling industrial recommender models has followed two parallel paradigms: \textbf{sample information scaling} -- enriching the information content of each training sample through deeper and longer behavior sequences -- and \textbf{model capacity scaling} -- unifying sequence modeling and feature interaction within a single Transformer backbone. However, these two paradigms still face two structural limitations. Firstly, sample information scaling methods encode only a subset of each historical interaction into the sequence token, leaving the majority of the original sample context unexploited and precluding the modeling of sample-level, time-varying features. Secondly, model capacity scaling methods are inherently constrained by the structural heterogeneity between sequential and non-sequential features, preventing the model from fully realizing its representational capacity. To address these issues, we propose \textbf{SIF} (\emph{Sample Is Feature}), which encodes each historical Raw Sample directly into the sequence token -- maximally preserving sample information while simultaneously resolving the heterogeneity between sequential and non-sequential features. SIF consists of two key components. The \textbf{Sample Tokenizer} quantizes each historical Raw Sample into a Token Sample via hierarchical group-adaptive quantization (HGAQ), enabling full sample-level context to be incorporated into the sequence efficiently. The \textbf{SIF-Mixer} then performs deep feature interaction over the homogeneous sample representations via token-level and sample-level mixing, fully unleashing the model's representational capacity. Extensive experiments on a large-scale industrial dataset validate SIF's effectiveness, and we have successfully deployed SIF on an industrial food delivery platform.

翻译：[摘要] 扩展工业推荐模型遵循两种并行范式：**样本信息扩展**——通过更深、更长的行为序列来丰富每个训练样本的信息内容——以及**模型容量扩展**——在单一Transformer骨干网络内统一序列建模和特征交互。然而，这两种范式仍面临两个结构性限制。首先，样本信息扩展方法仅将每个历史交互的一个子集编码到序列令牌中，导致原始样本上下文的大部分信息未被利用，并阻碍了对样本级时变特征的建模。其次，模型容量扩展方法本质上受到序列特征与非序列特征之间结构性异质性的限制，阻碍了模型充分发挥其表示能力。为解决这些问题，我们提出了**SIF**（*样本即特征*），该方法直接将每个历史原始样本编码到序列令牌中——最大限度地保留样本信息，同时解决序列特征与非序列特征之间的异质性。SIF由两个关键组件构成。**样本分词器**通过分层组自适应量化（HGAQ）将每个历史原始样本量化为令牌样本，从而有效地将完整的样本级上下文纳入序列。随后，**SIF-Mixer**通过令牌级和样本级混合，在同类样本表示上进行深度特征交互，充分释放模型的表示能力。在大规模工业数据集上的广泛实验验证了SIF的有效性，并且我们已成功将SIF部署于一个工业外卖平台。