quantification uncertainty recommender two-stage
Global Context Enhanced Graph Neural Networks for Session-based Recommendation
[TOC] > [Wang Z., Wei W., Cong G., Li X., Mao X. and Qiu M. Global context enhanced graph neural networks for session-based recommendation. SIGIR, 202 ......
Neural Attentive Session-based Recommendation
[TOC] >[ Li J., Ren P., Chen Z., Ren Z., Lian T. and Ma J. Neural attentive session-based recommendation. CIKM, 2017.](http://arxiv.org/abs/1711.04725 ......
Memory Priority Model for Session-based Recommendation
[TOC] > [Liu Q., Zeng Y., Mokhosi R. and Zhang H. STAMP: Short-term attention/memory priority model for session-based recommendation. KDD, 2018.](http ......
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
Tang J. and Wang K. Personalized top-n sequential recommendation via convolutional sequence embedding. WSDM, 2018. 概 序列推荐的经典之作, 将卷积用在序列推荐之上. 符号说明 $\ma ......
Handling Information Loss of Graph Neural Networks for Session-based Recommendation
Chen T. and Wong R. C. Handling information loss of graph neural networks for session-based recommendation. KDD, 2020. 概 作者发现图用在 Session 推荐中存在: lossy ......
解决 c3p0报错 Establishing SSL connection without server's identity verification is not recommended
解决 c3p0报错 Establishing SSL connection without server's identity verification is not recommended ?useSSL=false <c3p0-config> <default-config> <property ......
The Second Type of Uncertainty in Monte Carlo Tree Search
**发表时间：**2020 **文章要点：**MCTS里通常通过计算访问次数来做探索，这个被称作count-derived uncertainty。这篇文章提出了第二种uncertainty，这种uncertainty来源于子树的大小，一个直觉的想法就是，如果一个动作对应下的子树小，那就不用探索那么 ......
DiffuRec: A Diffusion Model for Sequential Recommendation
Li Z., Sun A. and Li C. DiffuRec: A diffusion model for sequential recommendation. arXiv preprint arXiv:2304.00686, 2023. 概 扩散模型用于序列推荐, 性能提升很大. DiffuR ......
Sequential Recommendation via Stochastic Self-Attention
Fan Z., Liu Z., Wang A., Nazari Z., Zheng L., Peng H. and Yu P. S. Sequential recommendation via stochastic self-attention. International World Wide W ......
youtube点击位置纠偏论文：《Recommending What Video to Watch Next: A Multitask Ranking System》
背景 在推荐系统存在两个难题： 1. 需要同时优化点击、观看时长、点赞、打分、评论等多个目标，如何同时建模多个目标 2. 存在position bias，即同个视频放在不通位置上点击率等会不同，如何建模position bias youtube这篇论文采用了MMOE来建模多目标，并用一个shallo ......
Uncertainty Quantification for Fairness in Two-Stage Recommender Systems
Wang L. and Joachims T. Uncertainty quantification for fairness in two-stage recommender systems. In International World Wide Web Conference (WWW), 20 ......
npm WARN deprecated core-js@2.6.12: core-js@<3.23.3 is no longer maintained and not recommended for usage due to the number of issues
npm WARN deprecated core-js@2.6.12: core-js@<3.23.3 is no longer maintained and not recommended for usage due to the number of issues. Because of the ......
Divide and Conquer: Towards Better Embedding-based Retrieval for Recommender Systems From a Multi-task Perspective
Zhang Y., Dong X., Ding W., Li B., Jiang P. and Gai K. Divide and Conquer: Towards better embedding-based retrieval for recommender systems from a mul ......
A Survey of Diversification Techniques in Search and Recommendation
Wu H., Zhang Y., Ma C., Lyu F., Diaz F. and Liu X. A survey of diversification techniques in search and recommendation. arXiv preprint arXiv:2212.1446 ......
Measuring the diversity of recommendations: a preference-aware approach for evaluating and adjusting diversity
Meymandpour R. and Davis J. G. Measuring the diversity of recommendations: a preference-aware approach for evaluating and adjusting diversity. Knowled ......
5.运行项目连接Mysql时出现警告Establishing SSL connection without server‘s identity verification is not recommende
jdbc:mysql://localhost:3306/testdb?useSSL=false 添加上述加粗部分,即可解决该警告,标红参数前面为你数据库连接URL,如果有多个参数记得用&连接,例如 jdbc:mysql://localhost:3306/testdb?characterEncodin ......
论文翻译：2023_THLNet: two-stage heterogeneous lightweight network for monaural speech enhancement
论文地址：THLNet: 用于单耳语音增强的两级异构轻量级网络 代码：https://github.com/dangf15/THLNet 引用格式：Dang F, Hu Q, Zhang P. THLNet: two-stage heterogeneous lightweight network f ......