期刊名称：Indian Journal of Computer Science and Engineering
印刷版ISSN：2231-3850
电子版ISSN：0976-5166
出版年度：2020
卷号：11
期号：3
页码：244-250
DOI：10.21817/indjcse/2020/v11i3/201103199
出版社：Engg Journals Publications
摘要：Recommender Systems are software techniques which can be used to filter out data from the volumes of data available online and provide recommendations to users in their area of interest. These techniques use information related to users and items in addition to the ratings given by users to various items or providing recommendations. In the last two decades, deep learning techniques have shown promising results in various areas of computer vision, video recognition, natural language processing etc. These techniques have been used for recommender systems in recent years and have shown improvement in performance. In this paper we propose a model, DeepAutoEnCF, that uses Denoising Autoencoder for predicting user ratings. It uses dropout for regularizing the model and adding noise to input for prediction of ratings. The model uses side information along with unique additional information for improving the performance.
关键词：Recommender Systems;Collaborative Filtering;Content Based Collaborative Filtering;Hybrid Systems;Memory Based Approach;Model Based Approach;Deep Learning ;Autoencoders;Dropout.
