526互联

lightgcl recommendation contrastive effective

D. Effects of Anti Pimples

D. Effects of Anti Pimples 对于样例一： 14出现2次 9出现1次 19出现12次 规律： 1.我们发现1与后面的组合的最大值等于数列的最大值，次数是2^(n-1),这是巧合吗？ 2.往下递推，我们可知2与后面的组合为2的倍数的最大值，次数为2^(n-2),... 3.因此 ......
Effects Pimples Anti of

Personalized Transformer for Explainable Recommendation论文阅读笔记

Personalized Transformer for Explainable Recommendation论文阅读笔记 摘要 ​ 自然语言生成的个性化在大量任务中都起着至关重要的作用。比如可解释的推荐，评审总结和对话系统等。在这些任务中，用户和项目ID是个性化的重要标识符。虽然Transfome ......
Recommendation Personalized Explainable Transformer 笔记

CF1877D Effects of Anti Pimples

计算每个数作为最大值的贡献，计算每个数作为最大值的次数。 每个数作为最大值时的贡献显然是 \(a_i\times cnt_i\)，\(cnt_i\) 为 \(a_i\) 在多少种染色方案中作为最大值出现，我们主要来对每个数求 \(cnt_i\)。 我们对于从 \(1\) 到 \(n\) 枚举元素，求 ......
Effects Pimples 1877D 1877 Anti

D. Effects of Anti Pimples

D. Effects of Anti Pimples Chaneka has an array $[a_1,a_2,\ldots,a_n]$. Initially, all elements are white. Chaneka will choose one or more different i ......
Effects Pimples Anti of

Effective C++——Item33: 避免隐藏继承的名字

Effective C++——Item33: 避免隐藏继承的名字 一、从原理理解隐藏 从变量作用域看隐藏 全局变量x和局部变量x的类型是不同的，但C++的隐藏规则：只隐藏名字(hiding names)。 int x; // global variable void someFunc() { dou ......
Effective 名字 Item 33

Effective C++——Item11: 在operator=()中处理自赋值问题

Item11: 在operator=()中处理自赋值问题 一、自赋值发生的时机： w = w，看起来不太可能发生，但可能隐式出现。 a[i] = a[j]，可能发生在数组循环中。 *p1 = *p2, p1 和 p2可能是来自一个继承体系中，指向相同对象的不同指针。 二、不安全实现：自赋值不安全，异 ......
Effective operator 问题 Item 11

CSS & JS Effect – Do something on enter/leave window tab

需求 我在做一个体验 当用户 submit enquiry 后会 window.open 开启 WhatsApp。而当用户关闭 WhatsApp 回来网站后，会 show 一个 feedback message。 实现思路 关键就在如何感知到，用户从 WhatsApp 切换回到了网站。 参考: De ......
something Effect window enter leave

Effective Modern C++

作者针对 C++11/14 而写的 Effective Modern C++ 简介 - Effective Modern C++ (cntransgroup.github.io) 一篇文章学完 Effective Modern C++：条款 & 实践 - 知乎 (zhihu.com) ......
Effective Modern

Unbiased Knowledge Distillation for Recommendation

目录概UnKD代码 Chen G., Chen J., Feng F., Zhou S. and He X. Unbiased knowledge distillation for recommendation. WSDM, 2023. 概 考虑流行度偏差的知识蒸馏, 应用于推荐系统. UnKD M ......
Recommendation Distillation Knowledge Unbiased for

报错： sudo: effective uid is not 0, is sudo installed setuid root?

切root用户报错 报错： sudo: effective uid is not 0, is sudo installed setuid root?解决 ： 给/usr/bin/sudo 文件添加一个 s 权限即可。 chmod +s /usr/bin/sudo 再切就可以了。 ......
sudo effective installed setuid is

DE-RRD: A Knowledge Distillation Framework for Recommender System

目录概DE-RRDDistillation Experts (DE)Relaxed Ranking Distillation (RRD)代码 Kang S., Hwang J., Kweon W. and Yu H. DE-RRD: A knowledge distillation framewor ......
Distillation Recommender Knowledge Framework DE-RRD

Topology Distillation for Recommender System

目录概Topology DistillationFull Topology Distillation (FTD)Hierarchical Topology Distillation (HTD)代码 Kang S., Hwang J., Kweon W. and Yu H. Topology dist ......
Distillation Recommender Topology System for

currently, chromedriver 114.0.5735.90 is recommended for chrome 114.*, so it is advised to delete the driver in PATH and retry

报错原因是驱动和浏览器不匹配 解决办法 1.下载低版本的谷歌浏览器 本次使用的是114 下载地址： https://downzen.com/en/windows/google-chrome/download/11405735199/ 2.下载谷歌浏览器的插件 https://registry.npm ......
chromedriver recommended currently 114 advised

Collaborative Distillation for Top-N Recommendation

目录概符号说明Collaborative distillation (CD) Lee J., Choi M., Lee J. and Shim H. Collaborative distillation for top-N recommendation. ICDM, 2019. 概 Ranking- ......
Recommendation Collaborative Distillation Top-N for

Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System

目录概符号说明Ranking Distillation代码 Tang J. and Wang K. Ranking Distillation: Learning compact ranking models with high performance for recommender system. ......
Ranking Distillation Performance Recommender Learning

Learning Heterogeneous Temporal Patterns of User Preference for Timely Recommendation

目录概符号说明TimelyRecMulti-aspect Time Encoder (MATE)Time-aware History Encoder (TAHE)Prediction代码 Cho J., Hyun D., Kang S. and Yu H. Learning heterogeneou ......
Recommendation Heterogeneous Preference Learning Temporal

Attention Mixtures for Time-Aware Sequential Recommendation

目录概符号说明MOJITO代码 Tran V., Salha-Galvan G., Sguerra B. and Hennequin R. Attention mixtures for time-aware sequential recommendation. SIGIR, 2023. 概 本文希望 ......
Recommendation Time-Aware Sequential Attention Mixtures

视频合成及特效制作-Adobe After Effects 2023 mac+win版

Adobe After Effects 2023 是一款专业的视频特效和动态图形制作软件。它是Adobe公司的产品之一，被广泛用于电影、电视、广告和网络视频制作中。 Adobe After Effects 2023 提供了强大的特效和动画工具，可以创建各种各样的视觉效果，包括粒子效果、光影效果、模糊 ......
特效 Effects Adobe After 视频

hugepages_settings.sh-Shell Script to Calculate Values Recommended Linux HugePages-HugeTLB Configuration_DocID401749.1

Oracle Linux-Shell Script to Calculate Values Recommended Linux HugePages-HugeTLB Configuration_DocID401749.1 ######################################## ......
Configuration_DocID hugepages_settings HugePages-HugeTLB Configuration Recommended

PSR是什么？PHP Standards Recommendations

PHP Standards Recommendations 官网：https://www.php-fig.org/psr/ PSR 是 PHP Standard Recommendations 的简写，由 PHP FIG 组织制定的 PHP 规范，是 PHP 开发的实践标准。 PHP FIG,FIG ......
Recommendations Standards PSR PHP

Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach

原文地址：https://arxiv.org/abs/2305.07001 本文作者将用户偏好、意图等构建为指令，并用这些指令调优一个LLM（3B Flan-T5-XL），该方法对用户友好，用户可以与系统交流获取更准确的推荐。 ## INTRODUCTION LLM是建立在自然语言文本上的，它不能直 ......
Recommendation Instruction Following Empowered Approach

How Expressive are Graph Neural Networks in Recommendation

[TOC] > [Cai X., Xia L., Ren X. and Huang C. How expressive are graph neural networks in recommendation? CIKM, 2023.](http://arxiv.org/abs/2308.11127) ......
Recommendation Expressive Networks Neural Graph

effective c++笔记

一. 截图 1. public继承，No32, Pg155 2. 不重定义继承而来的缺省参数值，No37, Pg183 ......
effective 笔记

Time Matters Sequential Recommendation with Complex Temporal Information

[TOC] > [Ye W., Wang S., Chen X., Wang X., Qin Z. and Yin D. Time Matters: Sequential recommendation with complex temporal information. SIGIR, 2020.]( ......
Recommendation Information Sequential Temporal Matters

通过提示大语言模型进行个性化推荐LLM-Rec: Personalized Recommendation via Prompting Large Language Models

论文原文地址：https://arxiv.org/abs/2307.15780 本文提出了一种提示LLM并使用其生成的内容增强推荐系统的输入的方法，提高了个性化推荐的效果。 ## LLM-Rec Prompting ![](https://img2023.cnblogs.com/blog/17994 ......
Recommendation Personalized Prompting Language 模型

Proj CDeepFuzz Paper Reading: Balancing Effectiveness and Flakiness of Non-Deterministic Machine Learning Tests

## Abstract 背景：In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of deep learning mod ......
Non-Deterministic Effectiveness Deterministic CDeepFuzz Balancing

A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis

摘要 基于方面的情感分析（ABSA）由于其广泛的应用，近年来受到了越来越多的关注。在现有的ABSA数据集中，大多数句子只包含一个或多个具有相同情感极性的方面，这使得ABSA任务退化为句子级情感分析。在本文中，我们提出了一个新的大规模多方面多情感（MAMS）数据集，其中每个句子至少包含两个具有不同情感 ......
Aspect-Based Challenge Effective Sentiment Analysis

A Contextualized Temporal Attention Mechanism for Sequential Recommendation

[TOC] > [Wu J., Cai R. and Wang H. D\'ej\`a vu: A contextualized temporal attention mechanism for sequential recommendation. WWW, 2020.](http://arxiv. ......
Contextualized Recommendation Sequential Attention Mechanism

Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer

[TOC] > [Fan Z., Liu Z., Zhang J., Xiong Y., Zheng L. and Yu P. S. Continuous-time sequential recommendation with temporal graph collaborative transfo ......
Continuous-Time Recommendation Collaborative Transformer Continuous

大模型时代的推荐系统Recommender Systems in the Era of Large Language Models (LLMs)

文章地址：https://arxiv.org/abs/2307.02046 笔记中的一些小实验中的模型都是基于GPT-3.5架构的ChatGPT模型。 本文主要讲述了比较具有代表性的方法利用LLM去学习user和item的表示，从预训练、微调和提示三个范式回顾了近期用于增强推荐系统的LLM先进技术， ......
Recommender Language 模型 Systems 时代
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