recommendation session-based information exploiting
论文解读（LightGCL）《LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation》
Note：[ wechat：Y466551 | 可加勿骚扰，付费咨询 ] 论文信息 论文标题：LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation论文作者：Cai, Xuheng and Huang, ......
HS-GCN Hamming Spatial Graph Convolutional Networks for Recommendation
[TOC] > [Liu H., Wei Y., Yin J. and Nie L. HS-GCN: Hamming spatial graph convolutional networks for recommendation. IEEE TKDE.](https://arxiv.org/pdf/ ......
[PWA] Get installed related information
const installedApps = await navigator.getInstalledRelatedApps() const packageId = "com.app.pwa" const app = installedApps.find(app => app.id packageId ......
农业工程与信息技术专业(Agricultural Engineering and Information Technology)
农业工程与信息技术专业的研究生考试情况。 （1）农业工程与信息技术是什么？ 农业工程与信息技术专业(Agricultural Engineering and Information Technology)，是一门集农业科学、环境生态工程、计算机科学、机械设备科学研究、工程项目科学研究、管理学等为一体 ......
dephi RTI （Runtime Type Information）获取运行时的控件信息
var Edit: TComponent;begin Edit := FindComponent("Edit1"); If Edit is TEdit then TEdit(Edit).Text := '你好 Delphi7';end; RTTI(RunTime Type Information): ......
Python的OCR工具pytesseract解决TesseractNotFoundError: tesseract is not installed or it's not in your PATH. See README file for more information环境变量问题
pytesseract是基于Python的OCR工具， 底层使用的是Google的Tesseract-OCR 引擎，支持识别图片中的文字，支持jpeg, png, gif, bmp, tiff等图片格式。 如何安装使用请看我的上一篇。 在使用pytesseract打开图片是遇到没有找到文件解决pyt ......
安装 MySQL for Windows 时报错 The configuration for MySQL Server 8.0.34 has failed. You can find more information about the failures in the 'Log' tab. 解决方法
今天在安装 MySQL for Windows 时报错 ```txt The configuration for MySQL Server 8.0.34 has failed. You can find more information about the failures in the 'Log' ......
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 ......
ansible构建失败 scp transfer mechanism failed on **** Use ANSIBLE DEBUG=1\nto see detailed information
ansible构建docker服务的失败排查经过（之前ansible构建成功） 第一步： 使用ansible 对应ip/或者在/etc/ansible/hosts中配置的label -m ping 查看当前连接对应服务器状态 对应失败服务器的连接状态 *@* * * * | FAILED! => { ......
information_schema系统数据库
1.schemata表 schema_name为mysql所有数据库的名字 2.tables表 table_schema为所有数据库的名字(不同于schema_name，它是一张表对应一个table_schema，数量大于等于总数据库数量) table_name为所有表的名字 3.columns表 ......
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 ......
The information of Seminars Language
‘The seminar is a common way ofteaching students on university courses in the UK and it is very likely that you will experience seminars on your cours ......
Exploiting Noise as a Resource for Computation and Learning in Spiking Neural Networks
郑重声明：原文参见标题，如有侵权，请联系作者，将会撤销发布！ https://arxiv.org/abs/2305.16044 Summary Keywords Introduction Results Noisy spiking neural network and noise-driven le ......
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: ......
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] ......
Kubernetes编程——client-go基础—— Informer 和缓存
Informer 和缓存 k8s 客户端接口中包含一个名叫 Watch 的动作，它提供了对集群对象变化（添加、删除或更新）进行响应的接口。Informer 在 Watch 的基础上对常见的使用场景提供了一个更高层的编程接口，包括：内存缓存以及通过名字对内存中的对象或属性进行查找的功能。 ......
ASCII = American Standard Code for Information Interchange
Text only语言: Ascii码表（全）ASCII Table (7-bit) (ASCII = American Standard Code for Information Interchange) Decimal Octal Hex Binary Value 000 000 00 0000 ......
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 ......
[6] Fast and Practical Secret Key Extraction by Exploiting Channel Response 论文精读 INFOCOM 13'
摘要 摘要写的很清楚，几句话说明了当前密钥发展现状，即使用RSS为基础的密钥生成解决方案的生成速率有待提升，因此本文主打一个高速率；此外本文提出了CGC算法来解决现实生活中的信道互易性差的问题；此外，其能够抵御被认为对RSS技术有害的恶意攻击！ 但是他的Abstract我有一点不满哈，全文都是CSI ......
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.] ......
Personal Information Exchange (PKCS #12)
The Personal Information Exchange format (PFX, also called PKCS #12) supports secure storage of certificates, private keys, and all certificates in a ......
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 ......