PCR-CA: Parallel Codebook Representations with Contrastive Alignment for Multiple-Category App Recommendation

TL;DR

PCR-CA introduces a novel framework with parallel codebook representations and contrastive alignment, significantly improving app recommendation accuracy, especially for long-tail apps, and has been successfully deployed in the Microsoft Store.

Contribution

The paper presents PCR-CA, a new end-to-end framework that uses parallel codebook VQ-AE and contrastive alignment to better model multiple-category app semantics and enhance recommendation performance.

Findings

Achieves +0.76% AUC improvement over baselines

Gains +2.15% AUC for long-tail apps

Online A/B testing shows +10.52% CTR lift

Abstract

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.