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This paper presents FA-RankMixer, a model that combines field-aware semantic tokenization, target-aware multi-domain DIN, RankMixer blocks, and group-wise bilinear fusion for unified pCVR prediction in the Tencent UNI-REC Challenge. The solution ranked 9th on the official leaderboard.
RecGPT-V3 introduces a stateful, hybrid-modal recommender that uses a Memory Hub and Latent Intent Reasoning to cut computation by 55.8% and output token cost by 200x, achieving significant gains in Taobao's feed.
OrDA is a framework that disentangles user access habits from genuine content interests in homepage marketing block recommendations using orthogonal regularization and causal intervention, achieving a 5.64% UCTR improvement on Zhima's homepage.
The paper proposes DIF, a model-agnostic method for denoising implicit feedback in cold-start recommendation by using pseudo-labels from content-similar warm items and uncertainty estimation, achieving significant improvements in a billion-user video app.
This paper introduces MedicalRec, a transformer-based recommender system that suggests optimal models for medical image classification tasks without retraining, built on a dataset (MedicalRec-Bench) compiled from 3,000 articles with over 5,000 records.
This paper presents a privacy-preserving federated recommendation system for mobile devices, using a two-stage pipeline with candidate generation and ranking, implemented via Kotlin Multiplatform on Android/iOS.