Generative Recommendation (GR) reframes retrieval and ranking as autoregressive decoding over Semantic IDs (SIDs), unifying the multi-stage pipeline into a single model. Yet a fundamental expressive gap persists: discriminative models score items with direct feature access enabling explicit user-item crossing, whereas GR decodes over compact SID tokens without item-side signal. We formalize this via Bayes' theorem: ranking by p(y|f,u) is equivalent to ranking by p(f|y,u), which factorizes autoregressively over item features, showing that a generative model with full feature access matches its discriminative counterpart, with any practical gap stemming solely from incomplete feature coverage.We propose UniRec with Chain-of-Attribute (CoA) as its core mechanism. CoA prefixes each SID sequence with structured attribute tokens:category, seller, brand, before decoding the SID, recovering the item-side feature crossing that discriminative models exploit. Since items sharing identical attributes cluster in adjacent SID regions, attribute conditioning yields a measurable per-step entropy reduction H(s_k|s<k,a) < H(s_k|s<k), narrowing the search space and stabilizing beam search. We further address two deployment challenges: Capacity-constrained SID introduces exposure-weighted capacity penalties into residual quantization to suppress token collapse and the Matthew effect; Conditional Decoding Context (CDC) combines Task-Conditioned BOS with hash-based Content Summaries to inject scenario signals at each decoding step. A joint RFT and DPO framework aligns the model with business objectives beyond distribution matching.Experiments show UniRec outperforms the strongest baseline by +22.6% HR@50 overall and +15.5% on high-value orders. Deployed on Shopee's e-commerce platform, online A/B tests confirm significant gains in PVCTR (+5.37%), orders (+4.76%), and GMV (+5.60%).

翻译：生成式推荐（GR）将检索与排序重新定义为对语义ID（SID）的自回归解码，将多阶段流水线统一为单一模型。然而，一个根本性的表达鸿沟依然存在：判别式模型通过直接特征访问对物品进行评分，从而实现显式的用户-物品交叉，而GR则仅基于紧凑型SID令牌进行解码，缺乏物品侧信号。我们通过贝叶斯定理对此进行形式化分析：基于p(y|f,u)排序等价于基于p(f|y,u)排序，后者对物品特征进行自回归分解，表明具备完整特征访问能力的生成模型与其判别式对应模型性能相当，任何实际差距均源于特征覆盖不完整。我们提出UniRec，其核心机制为属性链（CoA）。CoA在解码SID前，为每个SID序列添加结构化属性令牌（类别、卖家、品牌），从而恢复判别式模型所利用的物品侧特征交叉。由于共享相同属性的物品在相邻SID区域聚集，属性条件化可带来可量化的每步熵降低H(s_k|s<k,a) < H(s_k|s<k)，从而缩小搜索空间并稳定束搜索。我们进一步解决了两项部署挑战：容量受限型SID在残差量化中引入曝光加权容量惩罚，以抑制令牌坍缩与马太效应；条件解码上下文（CDC）将任务条件化起始符与基于哈希的内容摘要相结合，在每一步解码中注入场景信号。联合RFT与DPO框架使模型在分布匹配之外与业务目标对齐。实验表明，UniRec在整体HR@50指标上超越最强基线+22.6%，在高价值订单上提升+15.5%。部署于Shopee电商平台的在线A/B测试证实，PVCTR（+5.37%）、订单量（+4.76%）及GMV（+5.60%）均显著提升。