携程BI团队实践:推荐系统中基于深度学习的混合协同过滤模型
本文介绍了一些深度学习在推荐领域的应用,我们发现一些常见的深度模型(DNN,AE,CNN等)都可以应用于推荐系统中,但是针对不同领域的推荐,我们需要更多的高效的模型。随着深度学习技术的发展,我们相信深度学习将会成为推荐系统领域中一项非常重要的技术手段。 引用:[1] Ajit P. Singh,Geoffrey J. Gordon. “Relational Learning via Collective Matrix Factorization”,KDD 2008 [2] Suvash Sedhain,Aditya Krishna Menon,Scott Sanner,Lexing Xie. “AutoRec: Autoencoders Meet Collaborative Filtering”,WWW 2015 [3] Hao Wang,Naiyan Wang,Dit-Yan Yeung. “Collaborative Deep Learning for Recommender Systems”,KDD 2015 [4] Xin Dong,Lei Yu,ZhonghuoWu,Yuxia Sun,Lingfeng Yuan,Fangxi Zhang. “A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems”,AAAI 2017 [5] Paul Covington,Jay Adams,Emre Sargin. “Deep Neural Networks for YouTube Recommendations”,RecSys 2016 [6] Donghyun Kim,Chanyoung Park,Jinoh Oh,Sungyoung Lee,Hwanjo Yu. “Convolutional Matrix Factorization for Document Context-Aware Recommendation”,RecSys 2016 (编辑:晋中站长网) 【声明】本站内容均来自网络,其相关言论仅代表作者个人观点,不代表本站立场。若无意侵犯到您的权利,请及时与联系站长删除相关内容! |