Abstract
In recent years, although deep neural networks have yielded immense success in solving various recognition and classification problems, the exploration of deep neural networks in recommender systems has received relatively less attention. Meanwhile, the inherent sparsity of data is still a challenging problem for deep neural networks. In this paper, firstly, we propose a new CIDAE (Continuous Imputation Denoising Autoencoder) model based on the Denoising Autoencoder to alleviate the problem of data sparsity. CIDAE performs regular continuous imputation on the missing parts of the original data and trains the imputed data as the desired output. Then, we optimize the existing advanced NeuMF (Neural Matrix Factorization) model, which combines matrix factorization and a multi-layer perceptron. By optimizing the training process of NeuMF, we improve the accuracy and robustness of NeuMF. Finally, this paper fuses CIDAE and optimized NeuMF with reference to the idea of ensemble learning. We name the fused model the I-NMF (Imputation-Neural Matrix Factorization) model. I-NMF can not only alleviate the problem of data sparsity, but also fully exploit the ability of deep neural networks to learn potential features. Our experimental results prove that I-NMF performs better than the state-of-the-art methods for the public MovieLens datasets.
Highlights
In the era of information explosion, big data exhibits a rich value and great potential, which brings transformative development to human society, but it generates the serious “information overload”problem
The advantage of CIDAE is that it alleviates the problem of data sparsity by using the idea of imputation
We propose a new CIDAE model based on Denoising Autoencoder (DAE)
Summary
In the era of information explosion, big data exhibits a rich value and great potential, which brings transformative development to human society, but it generates the serious “information overload”. Recommender systems are an effective way to alleviate the problem of “information overload”, having been widely adopted by many online services, including E-commerce, online news, and social media sites [1]. Recommender systems can help determine which information to offer to individual consumers and allow online users to quickly find the personalized information that fits their needs [2]. CF [4,5] is based on the user’s past interaction records (such as ratings) to simulate the user’s preferences for the item. The scarcity of original interaction records has always been a difficult point for CF, which is the problem of data sparsity
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