diversity preference-aware recommendations evaluating
MCU之Microchip PIC16F17146 Curiosity NANO Evaluation Kit评测报告
对比完 RISC(Proprietary) 与 RISC-V(Open Source),来点 Microchip 的 PIC16F17146 Curiosity Nano(Revision 4 has PIC16F17146 rev B2) Evaluation Kit的实测： 这块板是多层PCB设 ......
[oeasy]python0073_进制转化_eval_evaluate_衡量_oct_octal_八进制
进制转化 回忆上次内容 上次了解的是 整型数字类变量 integer 前缀为i 添加图片注释，不超过 140 字（可选） 整型变量 和 字符串变量 不同 整型变量 是 直接存储二进制形式的 可以用 int()函数 将 2进制形式的 字符串 转化为 十进制整数 int()函数 接受两个变量 待转化 ......
Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning
图的作用： 图结构捕捉不同类型节点（即用户、项目和属性）之间丰富的关联信息，使我们能够发现协作用户对属性和项目的偏好。因此，我们可以利用图结构将推荐和对话组件有机地整合在一起，其中对话会话可以被视为在图中维护的节点序列，以动态地利用对话历史来预测下一轮的行动。 由四个主要组件组成：基于图的 MDP ......
粗读Multi-Task Recommendations with Reinforcement Learning
论文： Multi-Task Recommendations with Reinforcement Learning 地址： https://arxiv.org/abs/2302.03328 # 摘要 In recent years, Multi-task Learning (MTL) has yi ......
MEANTIME Mixture of Attention Mechanisms with Multi-temporal Embeddings for Sequential Recommendation
[TOC] > [Cho S., Park E. and Yoo S. MEANTIME: Mixture of attention mechanisms with multi-temporal embeddings for sequential recommendation. RecSys, 20 ......
Memory Augmented Graph Neural Networks for Sequential Recommendation
[TOC] > [Ma C., Ma L., Zhang Y., Sun J., Liu X. and Coates M. Memory augmented graph neural networks for sequential recommendation. AAAI, 2021.](http: ......
Effective Diversity in Population-Based Reinforcement Learning
 **发表时间：**2020 (NeurIPS 2020) **文章要点：**这篇文章提出了Diversity v ......
关于Deep Neural Networks for YouTube Recommendations的一些思考和实现
作者自己实现该文章的时候遇到的一些值得思考的地方： - [关于Deep Neural Networks for YouTube Recommendations的一些思考和实现](https://cloud.tencent.com/developer/article/1170340) - [备份网址] ......
【五期邹昱夫】CCF-B（IEEE Access'19）Badnets: Evaluating backdooring attacks on deep neural networks
> "Gu, Tianyu, et al. "Badnets: Evaluating backdooring attacks on deep neural networks." IEEE Access 7 (2019): 47230-47244." 本文提出了外包机器学习时选择值得信赖的提供商的重要 ......
Windows 11 Enterprise (Evaluation)下载地址
- [下载地址: https://developer.microsoft.com/en-us/windows/downloads/virtual-machines/](https://developer.microsoft.com/en-us/windows/downloads/virtual-ma ......
Automatic quality of generated text Evaluation for Large Language Models，针对大模型生成结果的自动化评测研究
Automatic quality of generated text Evaluation for Large Language Models，针对大模型生成结果的自动化评测研究 ......
Graph Masked Autoencoder for Sequential Recommendation
[TOC] > [Ye Y., Xia L. and Huang C. Graph masked autoencoder for sequential recommendation. SIGIR, 2023.](http://arxiv.org/abs/2305.04619) ## 概 图 + MA ......
混合性对话：Towards Conversational Recommendation over Multi-Type Dialogs
## 混合型对话 传统的人机对话研究专注于单一类型的对话，并且往往预设用户一开始就清楚对话目标。但实际应用中，人机对话常常混合了多种类型，例如闲聊、任务导向对话、推荐对话、问答等，并且用户目标是未知的。在这样的混合型对话中，机器人需要主动自然地进行对话推荐。 “混合型对话”这个新颖的任务于2020年 ......
Time Interval Aware Self-Attention for Sequential Recommendation
[TOC] > [Li J., Wang Y., McAuley J. Time interval aware self-attention for sequential recommendation. WSDM, 2020.](https://dl.acm.org/doi/10.1145/3336 ......
Exploiting Positional Information for Session-based Recommendation
[TOC] > [Qiu R., Huang Z., Chen T. and Yin H. Exploiting positional information for session-based recommendation. ACM Transactions on Information Syst ......
Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation
[TOC] > [Qiu R., Huang Z., Ying H. and Wang Z. Contrastive learning for representation degeneration problem in sequential recommendation. WSDM, 2022.] ......
Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation
[TOC] > [Xia X., Yin H., Yu J., Wang Q., Cui L and Zhang X. Self-supervised hypergraph convolutional networks for session-based recommendation. AAAI, ......
Self-Supervised Graph Co-Training for Session-based Recommendation
[TOC] > [Xia X., Yin H., Yu J., Shao Y. and Cui L. Self-supervised graph co-training for session-based recommendation. CIKM, 2021.](http://arxiv.org/a ......
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 ......
Exploring the Use of Humanized Mouse Models in Drug Safety Evaluation
However, there are differences between animals and humans, safety studies cannot be conducted on animal models alone, and normal animals do not respon... ......
IntelliJ idea evaluate expression
IntelliJ idea evaluate expression https://www.cnblogs.com/mrmoo/p/9942605.html ......
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 ......
Codeforces 1781G - Diverse Coloring（构造）
vp 时候想到大致思路了，但是死活调不对，赛后套取 cf 的数据调了好久才过/ll 首先直觉告诉我们答案不会太大。稍微猜一猜可以猜出除了四个点的菊花之外答案都是 $n\bmod 2$，下面我们来通过构造证明这件事情。 首先，链的情况是 trivial 的，直接根据奇偶性间隔染色即可。如果不是链，那么 ......
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
介绍 多视图立体重建是计算机视觉领域中一个非常重要的研究方向，它可以应用于三维建模、虚拟现实、机器人导航等多个领域。然而，目前多视图立体重建领域存在着很多问题和挑战，例如精度不高、完整性不足等。因此，作者希望通过本文对当前主流算法进行比较和评估，为该领域的进一步发展提供参考。 为了更准确地评估各种算 ......
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 ......