文献类型：期刊文献

英文题名：Auxiliary Stacked Denoising Autoencoder based Collaborative Filtering Recommendation

作者：Mu, Ruihui[1,2];Zeng, Xiaoqin[1]

第一作者：Mu, Ruihui;穆瑞辉

通讯作者：Mu, RH[1];Mu, RH[2]

机构：[1]Hohai Univ, Coll Comp & Informat, Nanjing 210098, Peoples R China;[2]Xinxiang Univ, Coll Comp & Informat Engn, Xinxiang 453000, Henan, Peoples R China

第一机构：Hohai Univ, Coll Comp & Informat, Nanjing 210098, Peoples R China

通讯机构：[1]corresponding author), Hohai Univ, Coll Comp & Informat, Nanjing 210098, Peoples R China;[2]corresponding author), Xinxiang Univ, Coll Comp & Informat Engn, Xinxiang 453000, Henan, Peoples R China.|[1107118]新乡学院计算机与信息工程学院;[11071]新乡学院;

年份：2020

卷号：14

期号：6

起止页码：2310-2332

外文期刊名：KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS

收录：;EI(收录号：20203809188045);Scopus(收录号：2-s2.0-85090848078);WOS:【SCI-EXPANDED(收录号:WOS:000548364300002)】；

基金：This research was supported by the National Key Research and Development Plan Key Projects of China under Grant No.2017YFC0405800, the National Natural Science Foundation of China Grant (Nos. 60971088, 60571048).

语种：英文

外文关键词：Auxiliary information; collaborative filtering; data sparsity; recommender system; stacked denoising autoencoder

摘要：In recent years, deep learning techniques have achieved tremendous successes in natural language processing, speech recognition and image processing. Collaborative filtering(CF) recommendation is one of widely used methods and has significant effects in implementing the new recommendation function, but it also has limitations in dealing with the problem of poor scalability, cold start and data sparsity, etc. Combining the traditional recommendation algorithm with the deep learning model has brought great opportunity for the construction of a new recommender system. In this paper, we propose a novel collaborative recommendation model based on auxiliary stacked denoising autoencoder(ASDAE), the model learns effective the preferences of users from auxiliary information. Firstly, we integrate auxiliary information with rating information. Then, we design a stacked denoising autoencoder based collaborative recommendation model to learn the preferences of users from auxiliary information and rating information. Finally, we conduct comprehensive experiments on three real datasets to compare our proposed model with state-of-the-art methods. Experimental results demonstrate that our proposed model is superior to other recommendation methods.