PCR-CA: Parallel Codebook Representations with Contrastive Alignment for Multiple-Category App Recommendation
arXiv:2508.18166v5 Announce Type: replace-cross 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 resid
The proliferation of complex, multi-functional applications on app stores necessitates more sophisticated recommendation systems that move beyond basic categorization.
Improved app recommendation directly impacts user engagement and revenue for app developers and platform providers, enhancing the efficiency of digital marketplaces.
Traditional, rigid app categorization methods will be increasingly augmented or replaced by AI-driven approaches capable of understanding nuanced and overlapping semantic meanings.
- · App store platforms (e.g., Apple, Google)
- · AI/ML researchers in recommendation systems
- · Developers of multi-category applications
- · Consumers seeking more relevant app suggestions
- · Traditional content categorization services
- · App developers reliant on simple keyword-based discoverability
More accurate app recommendations lead to higher user satisfaction and engagement on app platforms.
Increased engagement drives more opportunities for app monetization and advertising revenue for platform owners.
The success of advanced AI in app recommendation could accelerate its adoption in other complex content domains like streaming media or e-commerce, creating more personalized digital ecosystems.
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Read at arXiv cs.LG