论文: 论文题目:《One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain C...
论文: 论文题目:《One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain C...
论文: 论文题目:《A Dual Augmented Two-tower Model for Online Large-scale Recommendation》 论文地址:...
论文: 论文题目:《DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Lea...
论文: 论文题目:《Distillation based Multi-task Learning: A Candidate Generation Model for Impr...
论文: 地址:https://arxiv.org/pdf/2102.09267.pdf 论文题目:《Sparse-Interest Network for Sequentia...
论文: 论文题目:《An Input-aware Factorization Machine for Sparse Prediction》 论文地址:https://www....
论文: 论文题目:《User Behavior Retrieval for Click-Through Rate Prediction》 论文地址:https://arxiv...
论文: 论文题目:《Unclicked User Behaviors Enhanced Sequential Recommendation》 论文地址:https://arx...
论文: 论文题目:《Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations...
论文: 论文题目:《Deep Multi-Interest Network for Click-through Rate Prediction》 论文地址:https://d...
论文: 论文题目:《Deep Time-Aware Item Evolution Network for Click-Through Rate Prediction》 论文地...
论文: 论文题目:《PAL: A Position-bias Aware Learning Framework for CTR Prediction in Live Reco...
论文: 论文题目:《Real-time Attention Based Look-alike Model for Recommender System》 论文地址:https...
@Seeumt 那就直接用item+lr吧
推荐系统论文阅读(四十二)-阿里:融合Match和Rank的DMR模型论文: 论文题目:《Deep Match to Rank Model for Personalized Click-Through Rate Prediction》 论文地址...
论文: 论文题目:《Controllable Multi-Interest Framework for Recommendation》 论文地址:https://arxiv....
你就把经典的召回模型和排序模型的github源码找出来,然后弄个movielens数据跑通就好了,召回可以使用dssm,排序用din就可以,加油小伙子~
推荐系统论文阅读(四十二)-阿里:融合Match和Rank的DMR模型论文: 论文题目:《Deep Match to Rank Model for Personalized Click-Through Rate Prediction》 论文地址...
论文: 论文题目:《Deep Match to Rank Model for Personalized Click-Through Rate Prediction》 论文地址...
论文: 论文题目:《Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR ...
@柠樂helen 1.x是用户的历史行为序列 2.物品的内容向量一般是通过nlp或者cv模型来得到,输入的item是用户的历史行为序列,负样本不需要构造,因为模型中除了用户感兴趣的物品外,其余物品都是负样本 这只是我的个人理解,希望可以讨论下
推荐系统论文阅读(一)-序列推荐结合长尾物品提升推荐的多样性疫情在家阅读了大量了推荐系统论文,但是都没有好好的写过博客,基本上都是精读过后只记得论文的思想,重新阅读之前的论文还会对有些数学公式一知半解。基于这方面的考虑,还是决定在阅读...