Modern app store recommender systems struggle with multiple-category apps, as traditional taxonomies fail to capture overlapping semantics, leading to suboptimal personalization. We propose PCR-CA (Parallel Codebook Representations with Contrastive Alignment), an end-to-end framework for improved CTR prediction. PCR-CA first extracts compact multimodal embeddings from app text, then introduces a Parallel Codebook VQ-AE module that learns discrete semantic representations across multiple codebooks in parallel -- unlike hierarchical residual quantization (RQ-VAE). This design enables independent encoding of diverse aspects (e.g., gameplay, art style), better modeling multiple-category semantics. To bridge semantic and collaborative signals, we employ a contrastive alignment loss at both the user and item levels, enhancing representation learning for long-tail items. Additionally, a dual-attention fusion mechanism combines ID-based and semantic features to capture user interests, especially for long-tail apps. Experiments on a large-scale dataset show PCR-CA achieves a +0.76% AUC improvement over strong baselines, with +2.15% AUC gains for long-tail apps. Online A/B testing further validates our approach, showing a +10.52% lift in CTR and a +16.30% improvement in CVR, demonstrating PCR-CA's effectiveness in real-world deployment. The new framework has now been fully deployed on the Microsoft Store.

翻译：摘要：现代应用商店推荐系统在处理多类别应用时面临挑战，传统分类体系难以捕捉重叠语义，导致个性化效果欠佳。本文提出PCR-CA（基于对比对齐的并行码本表征），一种端到端框架以提升点击率预测性能。PCR-CA首先从应用文本中提取紧凑多模态嵌入，进而引入并行码本VQ-AE模块——区别于层次化残差量化（RQ-VAE），该模块可并行学习跨多个码本的离散语义表征。这种设计能够独立编码不同维度属性（如玩法、美术风格），更有效地建模多类别语义。为弥合语义信号与协同信号，我们在用户和物品层面采用对比对齐损失，增强长尾物品的表征学习。此外，双注意力融合机制将ID特征与语义特征相结合以捕获用户兴趣，尤其针对长尾应用。大规模数据集实验表明，PCR-CA相较强基线方法实现AUC提升0.76%，其中长尾应用AUC提升2.15%。在线A/B测试进一步验证了方法有效性：点击率提升10.52%，转化率提升16.30%，证明了PCR-CA在实际部署中的有效性。该框架现已完整部署于微软应用商店。