文件名称：推荐系统2018经典论文
文件大小：9.88MB
文件格式：ZIP
更新时间：2021-03-27 13:41:50
推荐系统 机器学习 论文
10篇经典的推荐系统文章，Reinforcement Learning based Recommender System using Biclustering Technique；Learning Continuous User Representations through Hybrid Filtering with doc2vec；Deep Reinforcement Learning for List-wise Recommendations；Leveraging Long and Short-term Information in Content-aware Movie Recommendation；Deep Collaborative Autoencoder for Recommender Systems: A Unified Framework for Explicit and Implicit Feedback；Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works；A Context-Aware User-Item Representation Learning for Item Recommendation；Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time；Recommender Systems with Random Walks: A Survey；Deep Learning Based Recommender System: a Survey and New Perspectives；Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios；A Deep Multimodal Approach for Cold-start Music Recommendation
【文件预览】：
推荐系统论文2018
----Deep Collaborative Autoencoder for Recommender Systems A Unified Framework for Explicit and Implicit Feedback.pdf(1.27MB)
----A Deep Multimodal Approach for Cold-start Music Recommendation.pdf(690KB)
----Deep Learning Based Recommender System a Survey and New Perspectives.pdf(2.58MB)
----Reinforcement Learning based Recommender System using Biclustering Technique.pdf(898KB)
----推荐系统.txt(54B)
----Recommender Systems with Random Walks A Survey.pdf(186KB)
----A Context-Aware User-Item Representation Learning for Item Recommendation.pdf(473KB)
----Learning Continuous User Representations through Hybrid Filtering with doc2vec.pdf(827KB)
----Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios.pdf(813KB)
----Leveraging Long and Short-term Information in Content-aware Movie Recommendation.pdf(1.4MB)
----Use of Deep Learning in Modern Recommendation System A Summary of Recent Works.pdf(444KB)
----Deep Reinforcement Learning for List-wise Recommendations.pdf(959KB)
----Pixie A System for Recommending 3 Billion Items to 200 Million Users in Real-Time.pdf(678KB)