Deep neural networks meet recommender systems

The data available online, helps users to get information about anything of his/her interest. But since the data is huge and complex it is difficult to get useful information from it. Recommender Systems are effective software techniques to overcome this problem. Based on the user’s and item’s information available, these techniques provide recommendations to users in their area of interest. Recommender systems have wide applications like providing suggestive list of items to customers for online shopping, recommending articles or books for online reading, movie or music recommendations, news recommendations etc. In this paper we provide a study of Deep Neural Networks (DNN) approaches that can be used for recommender systems. They have been used widely in last decade in many fields like image processing, video streaming, Natural Language Processing etc. including recommendations to overcome the drawbacks of traditional systems. The paper also provides performance of Denoising AutoEncoders (DAE) which are feed forward neural networks and its comparison with traditional systems. Denoising Autoencoders are a type of autoencoders wherein some part of input is corrupted, i.e., noise is added to the input. While learning to remove noise from input, the DAE also learns to predict unknown values. This property of Denoising Autoencoders can help in recommendation systems to predict unknown values before recommending new items. Experimentation has shown improvement in the performance of recommendation systems with denoising autoencoders. The evaluation is performed on MovieLens-1M dataset with and without additional features of users (age and gender) and items (movie genres) provided in the dataset.

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Tag-aware recommender systems based on deep neural networks

Neurocomputing ◽

10.1016/j.neucom.2015.10.134 ◽

2016 ◽

Vol 204 ◽

pp. 51-60 ◽

Cited By ~ 49

Author(s):

Yi Zuo ◽

Jiulin Zeng ◽

Maoguo Gong ◽

Licheng Jiao

Keyword(s):

Neural Networks ◽

Recommender Systems ◽

Deep Neural Networks

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Dual-regularized matrix factorization with deep neural networks for recommender systems

Knowledge-Based Systems ◽

10.1016/j.knosys.2018.01.003 ◽

2018 ◽

Vol 145 ◽

pp. 46-58 ◽

Cited By ~ 44

Author(s):

Hao Wu ◽

Zhengxin Zhang ◽

Kun Yue ◽

Binbin Zhang ◽

Jun He ◽

...

Keyword(s):

Neural Networks ◽

Recommender Systems ◽

Matrix Factorization ◽

Deep Neural Networks

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Recommender systems based on opinion mining and deep neural networks

MATEC Web of Conferences ◽

10.1051/matecconf/201817303016 ◽

2018 ◽

Vol 173 ◽

pp. 03016 ◽

Cited By ~ 1

Author(s):

Jia Li ◽

YongJian Yang

Keyword(s):

Neural Networks ◽

Prediction Model ◽

Recommender Systems ◽

Text Analysis ◽

Deep Neural Networks ◽

Opinion Mining ◽

Mining Method ◽

Rating Prediction ◽

Sparsity Problem ◽

Public Datasets

To address rating sparsity problem, various review-based recommender systems have been developed in recent years. Most of them extract topics, opinions, and emotional polarity from the reviews by using the techniques of text analysis and opinion mining. According to existing researches, review-based recommendation methods utilize review elements in rating prediction model, but underuse the actual ratings provided by users. In this paper, we adopt one lexicon-based opinion mining method to extract opinions hidden in reviews, and also, we combine opinions with actual ratings. In addition, we embed deep neural networks model which breaks through the limitation of traditional collaborative filtering. The experimental results based on two public datasets indicate that this personalized model provides an effective recommendation performance.

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