526互联

embedding-based recommender perspective multi-task

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

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

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

Position-Enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations

# Position-Enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations [TOC] > [Huang L., Ma Y., Liu Y., Du B., Wang S. and Li ......
Position-Enhanced Recommendations Convolutional Time-aware Sequential

Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)

长尾问题是个老大难问题了。 在推荐中可以是用户/物料冷启动，在搜索中可以是中低频query、文档，在分类问题中可以是类别不均衡。长尾数据就像机器学习领域的一朵乌云，飘到哪哪里就阴暗一片。今天就介绍来自Google的一篇解决长尾物品推荐的论文。 ......
Recommendation Empowering Decoupling Long-tail Network

Time-aware Path Reasoning on Knowledge Graph for Recommendation

[TOC] > [Zhao Y., Wang X., Chen J., Wang Y., Tang W., He X. and Xie H. Time-aware path reasoning on knowledge graph for recommendation. TOIS, 2022.](h ......
Recommendation Time-aware Knowledge Reasoning Graph

Interface from multiple perspectives

> Interface is a broad concept, So to understand it please use multiple perspectives. 中文日：君子不器。 # Generalization - `事件处理规范`: 如API中的EventListener、Actio ......
perspectives Interface multiple from

How Can Recommender Systems Benefit from Large Language Models: A Survey 阅读笔记

论文主要从LLM应用在推荐系统哪些部分以及LLM如何应用在推荐系统中，还讨论了目前LLM应用在RS中的一些问题。 ###Where? 推荐系统哪些部分哪里可以应用到大模型？文章中提到了特征工程、特征编码、评分/排序函数、推荐流程控制。 - LLM for Feature Engineering - ......
Recommender Language Benefit Systems 笔记

[KDD 2023] All in One- Multi-Task Prompting for Graph Neural Networks

# [KDD 2023] All in One- Multi-Task Prompting for Graph Neural Networks ## 总结 提出了个多任务prompt学习框架，扩展GNN的泛化能力： 1. 统一了NLP和图学习领域的prompt格式，包括prompt token、to ......
Multi-Task Prompting Networks Neural Graph

A Neural Influence Diffusion Model for Social Recommendation

[TOC] > [Wu L., Sun P., Fu Y., Hong R., Wang X. and Wang M. A neural influence diffusion model for social recommendation. SIGIR, 2019.](https://dl.acm ......
Recommendation Influence Diffusion Neural Social

SocialLGN Light graph convolution network for social recommendation

[TOC] > [Liao J., Zhou W., Luo F., Wen J., Gao M., Li X. and Zeng J. SocialLGN: Light graph convolution network for social recommendation. Information ......
recommendation convolution SocialLGN network social

PnP and Perspective Projection and Pose Computation

# PnP and Perspective Projection and Pose Computation *Review PnP problem from a computer graphics rendering view* 首先从一个 [StackExchange](https://compu ......
Perspective Computation Projection and Pose

论文解读（SimGCL）《Are Graph Augmentations Necessary? Simple 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：Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation论文作者：Junliang Yu ......
Graph Recommendation Augmentations Contrastive Necessary

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, ......
LightGCL Recommendation Contrastive Effective Learning

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/ ......
Recommendation Convolutional Networks Hamming Spatial

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 ......
Learning Conversational Recommendation Reinforcement Graph-based

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 ......
Recommendations Reinforcement Multi-Task Learning Multi

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 ......
Multi-temporal Recommendation Embeddings Mechanisms Sequential

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: ......
Recommendation Sequential Augmented Networks Memory

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] ......
Recommendations Networks YouTube Neural Deep

论文阅读 | Soteria: Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective

Soteria:基于表示的联邦学习中可证明的隐私泄露防御https://ieeexplore.ieee.org/document/9578192 # 3 FL隐私泄露的根本原因 ## 3.1 FL中的表示层信息泄露 **问题设置** 在FL中，有多个设备和一个中央服务器。服务器协调FL进程，其中每个 ......
Representation Perspective Federated Provable Learning

Probabilistic and Geometric Depth: Detecting Objects in Perspective(1)

作者认为单目3D目标检测可以简化为深度估计问题,深度估计不准确限制了检测的性能.已有的算法直接使用孤立实例或者像素估计深度,没有考虑目标之间的集合关系,因此提出了构建预测的目标之间的几何关系图,来促进深度预测. 将深度值划分成若干个区间,然后通过分布的期望来计算深度值,在精度和速度上都取得了不错的性 ......
Probabilistic Perspective Geometric Detecting Objects

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 ......
Recommendation Autoencoder Sequential Masked Graph

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 ......
混合性 Conversational Recommendation Multi-Type Towards

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 ......
Self-Attention Recommendation Sequential Attention Interval

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 ......
Recommendation Session-based Information Exploiting Positional

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.] ......
Representation Recommendation Degeneration Contrastive Sequential
共84篇  :2/3页 首页上一页2下一页尾页