Climber: Toward Efficient Scaling Laws for Large Recommendation Models
一个 CTR 领域更加高效 Scaling 的推荐架构
Recommendation
RankMixer: Scaling Up Ranking Models in Industrial Recommenders
抖音在大模型推荐上的尝试
Progressive Semantic Residual Quantization for Multimodal-Joint Interest Modeling in Music Recommendation
对残差量化的修改
HLLM-Creator: Hierarchical LLM-based Personalized Creative Generation
利用 LLM 高效生成个性化广告语
On the Reliability of Sampling Strategies in Offline Recommender Evaluation
不同采样策略在不同曝光偏差下的区分性, 鲁棒性, 一致性
SEvo
Is Every Item Worth An Embedding?
是否每个 Item 都值得一个可学习的 Embedding 呢