Improved hybrid recommendation algorithm based on stacked denoising autoencoder
YANG Shuai1, WANG Juan2
1. National Engineering Research Center for Multimedia Software(School of Computer Science, Wuhan University), Wuhan Hubei 430072, China; 2. Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education(School of Computer Science, Wuhan University), Wuhan Hubei 430072, China
Abstract：Concerning the problem that traditional collaborative filtering algorithm just utilizes users' ratings on items when generating recommendation, without considering users' labels or comments, which can not reflect users' real preference on different items and the prediction accuracy is not high and easily overfits, a Stacked Denoising AutoEncoder (SDAE)-based improved Hybrid Recommendation (SDHR) algorithm was proposed. Firstly, SDAE was used to extract items' explicit features from users' free-text labels. Then, Latent Factor Model (LFM) algorithm was improved, the LFM's abstract item features were replaced with extracted explicit ones to train matrix decomposition model. Finally, the user-item preference matrix was used to generate recommendations. Experimental tests on the dataset MovieLens showed that the accuracy of the proposed algorithm was improved by 38.4%, 16.1% and 45.2% respectively compared to the three recommendation models (including the model based on label-based weights with collaborative filtering, the model based on SDAE and extreme learning machine, and the model based on recurrent neural networks). The experimental results show that the proposed algorithm can make full use of items' free-text label information to improve recommendation performance.
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2018, Vol. 38 
