Recommender Systems with Deep Learning

Improving Scalability of Personalized Recommendation Systems for Enterprise Knowledge Workers
– Authors: C Verma, M Hart, S Bhatkar, A Parker (2016)
Multi-modal learning for video recommendation based on mobile application usage
– Authors: X Jia, A Wang, X Li, G Xun, W Xu, A Zhang (2016)
Collaborative Filtering with Stacked Denoising AutoEncoders and Sparse Inputs
– Authors: F Strub, J Mary (2016)
Applying Visual User Interest Profiles for Recommendation and Personalisation
– Authors: J Zhou, R Albatal, C Gurrin (2016)
Comparative Deep Learning of Hybrid Representations for Image Recommendations
– Authors: C Lei, D Liu, W Li, Zj Zha, H Li (2016)
Tag-Aware Recommender Systems Based on Deep Neural Networks
– Authors: Y Zuo, J Zeng, M Gong, L Jiao (2016)
Quote Recommendation in Dialogue using Deep Neural Network
– Authors: H Lee, Y Ahn, H Lee, S Ha, S Lee (2016)
Toward Fashion-Brand Recommendation Systems Using Deep-Learning: Preliminary Analysis
– Authors: Y Wakita, K Oku, K Kawagoe (2016)
Word embedding based retrieval model for similar cases recommendation
– Authors: Y Zhao, J Wang, F Wang (2016)
ConTagNet: Exploiting User Context for Image Tag Recommendation
– Authors: Ys Rawat, Ms Kankanhalli (2016)
Wide & Deep Learning for Recommender Systems
– Authors: Ht Cheng, L Koc, J Harmsen, T Shaked, T Chandra… (2016)
On Deep Learning for Trust-Aware Recommendations in Social Networks.
– Authors: S Deng, L Huang, G Xu, X Wu, Z Wu (2016)
A Survey and Critique of Deep Learning on Recommender Systems
– Authors: L Zheng (2016)
Collaborative Filtering and Deep Learning Based Hybrid Recommendation for Cold Start Problem
– Authors: J Wei, J He, K Chen, Y Zhou, Z Tang (2016)
Collaborative Filtering and Deep Learning Based Recommendation System For Cold Start Items
– Authors: J Wei, J He, K Chen, Y Zhou, Z Tang (2016)
Deep Neural Networks for YouTube Recommendations
– Authors: P Covington, J Adams, E Sargin (2016)
Towards Latent Context-Aware Recommendation Systems
– Authors: M Unger, A Bar, B Shapira, L Rokach (2016)
Automatic Recommendation Technology for Learning Resources with Convolutional Neural Network
– Authors: X Shen, B Yi, Z Zhang, J Shu, H Liu (2016)
Tag-Aware Personalized Recommendation Using a Deep-Semantic Similarity Model with Negative Sampling
– Authors: Z Xu, C Chen, T Lukasiewicz, Y Miao, X Meng (2016)
Latent Factor Representations for Cold-Start Video Recommendation
– Authors: S Roy, Sc Guntuku (2016)
Convolutional Matrix Factorization for Document Context-Aware Recommendation
– Authors: D Kim, C Park, J Oh, S Lee, H Yu (2016)
Conversational Recommendation System with Unsupervised Learning
– Authors: Y Sun, Y Zhang, Y Chen, R Jin (2016)
RecSys’ 16 Workshop on Deep Learning for Recommender Systems (DLRS)
– Authors: A Karatzoglou, B Hidasi, D Tikk, O Sar (2016, Workshop proceedings)
Ask the GRU: Multi-task Learning for Deep Text Recommendations
– Authors: T Bansal, D Belanger, A Mccallum (2016)
Recurrent Coevolutionary Latent Feature Processes for Continuous-Time Recommendation
– Authors: H Dai, Y Wang, R Trivedi, L Song (2016)
Keynote: Deep learning for audio-based music recommendation
– Authors: S Dieleman (2016)
Tumblr Blog Recommendation with Boosted Inductive Matrix Completion
– Authors: D Shin, S Cetintas, Kc Lee, Is Dhillon (2015)
Deep Collaborative Filtering via Marginalized Denoising Auto-encoder
– Authors: S Li, J Kawale, Y Fu (2015)
Learning Image and User Features for Recommendation in Social Networks
– Authors: X Geng, H Zhang, J Bian, Ts Chua (2015)
UCT-Enhanced Deep Convolutional Neural Network for Move Recommendation in Go
– Authors: S Paisarnsrisomsuk (2015)
A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems
– Authors: A Elkahky, Y Song, X He (2015)
It Takes Two to Tango: An Exploration of Domain Pairs for Cross-Domain Collaborative Filtering
– Authors: S Sahebi, P Brusilovsky (2015)
Latent Context-Aware Recommender Systems
– Authors: M Unger (2015)
Learning Distributed Representations from Reviews for Collaborative Filtering
– Authors: A Almahairi, K Kastner, K Cho, A Courville (2015)
A Collaborative Filtering Approach to Real-Time Hand Pose Estimation
– Authors: C Choi, A Sinha, Jh Choi, S Jang, K Ramani (2015)
Collaborative Deep Learning for Recommender Systems
– Authors: H Wang, N Wang, Dy Yeung (2014)
CARS2: Learning Context-aware Representations for Context-aware Recommendations
– Authors: Y Shi, A Karatzoglou, L Baltrunas, M Larson, A Hanjalic (2014)
Relational Stacked Denoising Autoencoder for Tag Recommendation
– Authors: H Wang, X Shi, Dy Yeung (2014)

DLRS(近三年深度学习应用于推荐系统论文汇总)的更多相关文章

  1. DLRS(深度学习应用于推荐系统论文汇总--2017年8月整理)

    Recommender Systems with Deep Learning Alessandro:ADAAlessandro Suglia, Claudio Greco, Cataldo Musto ...

  2. [置顶] 人工智能(深度学习)加速芯片论文阅读笔记 (已添加ISSCC17,FPGA17...ISCA17...)

    这是一个导读,可以快速找到我记录的关于人工智能(深度学习)加速芯片论文阅读笔记. ISSCC 2017 Session14 Deep Learning Processors: ISSCC 2017关于 ...

  3. 深度学习应用在推荐系统的论文-----A Novel Deep Learning-Based Collaborative Filtering Model for Recommendation System

    1.题目:一种新的基于深度学习的协同过滤推荐系统 2.摘要: 以协同过滤(CF)为基础的模型主要获取用户和项目的交互或者相关性.然而,现有的基于CF的方法只能掌握单一类型的关系,如RBM,它只能获取用 ...

  4. arXiv 2015深度学习年度十大论文

    由康奈尔大学运营维护着的arXiv网站,是一个在学术论文还未被出版时就将之向所有人开放的地方.这里汇聚了无数科学领域中最前沿的研究,机器学习也包括在内.它反映了学术界当前的整体趋势,我们看到,近来发布 ...

  5. python 深度学习 库文件安装出错汇总

    Cython_bbox FairMOT | win10下cython-bbox安装的心酸之路_是阳阳呀的博客-CSDN博客 swig 安装polyiou.py https://blog.csdn.ne ...

  6. 推荐系统遇上深度学习(十)--GBDT+LR融合方案实战

    推荐系统遇上深度学习(十)--GBDT+LR融合方案实战 0.8012018.05.19 16:17:18字数 2068阅读 22568 推荐系统遇上深度学习系列:推荐系统遇上深度学习(一)--FM模 ...

  7. 【深度学习Deep Learning】资料大全

    最近在学深度学习相关的东西,在网上搜集到了一些不错的资料,现在汇总一下: Free Online Books  by Yoshua Bengio, Ian Goodfellow and Aaron C ...

  8. 经典书单 —— 语言/算法/机器学习/深度学习/AI/CV/PGM

    0.0 计算机科学 <Lex 与 Yacc> Think Complexity(使用 Python 语言) GitHub - AllenDowney/ThinkComplexity: Co ...

  9. SIGAI深度学习第四集 深度学习简介

    讲授机器学习面临的挑战.人工特征的局限性.为什么选择神经网络.深度学习的诞生和发展.典型的网络结构.深度学习在机器视觉.语音识别.自然语言处理.推荐系统中的应用 大纲: 机器学习面临的挑战 特征工程的 ...

随机推荐

  1. zabbix服务端安装指南及常见问题解决

    1. 首先要准备LNMP环境 2. 在mysql中创建zabbix所需要的库和用户 mysql -uroot -pmysql> CREATE DATABASE zabbix CHARACTER ...

  2. 关于python单方法的类

    1.大部分情况下,你拥有一个单方法类的原因是需要存储某些额外的状态来给方法使用. 此种情况下可以使用闭包代替,参考 javascript的闭包计数器实现,python实现各种方法来实现计数器 关于这个 ...

  3. Metropolis-Hastings算法

    (学习这部分内容大约需要1.5小时) 摘要 马尔科夫链蒙特卡洛(Markov chain Monte Carlo, MCMC)是一种近似采样算法, 它通过定义稳态分布为 \(p\) 的马尔科夫链, 在 ...

  4. php根据地理坐标获取国家、省份、城市,及周边数据类

    功能:当App获取到用户的地理坐标时,可以根据坐标知道用户当前在那个国家.省份.城市,及周边有什么数据. 原理:基于百度Geocoding API 实现,需要先注册百度开发者,然后申请百度AK(密钥) ...

  5. Eclipse------使用Debug As时报错java.lang.IllegalStateException: Failed to read Class-Path attribute from manifest of jar file:/XXX

    报错信息: java.lang.IllegalStateException: Failed to read Class-Path attribute from manifest of jar file ...

  6. 8 -- 深入使用Spring -- 2... Spring的“零配置”支持

    8.2 Spring的“零配置”支持 Spring支持使用Annotation来代替XML配置文件.

  7. zabbix中Templates的jmx相关key调试方法

    1.下载 cmdline jmxclient 如果你有一个完美的模版,你可能可以忽略此步.但是大多数情况下你没有.况且 zabbix 默认的 tomcat 模版也不能很好的工作.这时候有一个工具来调试 ...

  8. 【代码审计】EasySNS_V1.6远程图片本地化导致Getshell

    0x00 环境准备 EasySNS官网:http://www.imzaker.com 网站源码版本:EasySNS极简社区V1.60 程序源码下载:http://es.imzaker.com/inde ...

  9. mysql 外键约束示例

    -- 创建测试主表. ID 是主键.CREATE TABLE test_main (  id      INT,  value   VARCHAR(10),  PRIMARY KEY(id)); -- ...

  10. linux 开机自启脚本

    1.vi /home/dpf/mqtt.sh #!/bin/sh/home/dpf/Desktop/Udp_Single_Async_Mqtt_yuan/hwjc_udp_receive_mqtt & ...