arXiv Machine Learning June 15, 2026 By Bin Tan, Wangyao Ge, Yidi Wang, Xin Liu, Jeff Burtoft, Hao Fan, Hui Wang

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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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PaletteID: Prototype-Composed Semantic Identifiers for Multimodal CTR Prediction

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arXiv Machine Learning

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GALA: Generative Aligned Learning for Adaptive Multimodal Representation in the Taobao Shangou Recommender System

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arXiv AI

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The Best of the Two Worlds: Harmonizing Semantic and Hash IDs for Sequential Recommendation

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PaletteID: Prototype-Composed Semantic Identifiers for Multimodal CTR Prediction

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