Auto-Encoder based Recommendation Algorithm Combining Item Types

Tianci Wang

《Frontiers in Economics and Management》 2卷3期 (2021/03) Pp. 321-327

https://doi.org/10.6981/FEM.202103_2(3).0040

摘要

Traditional collaborative filtering algorithm relies on score matrix to generate prediction, fails to mine potential features related to item genres, which leads to a low recommendation accuracy. A denoising autoencoder based collaborative filtering recommendation algorithm combining item types is proposed. Firstly, combining item score data and genres data, the potential features of the items are extracted by denoising autoencoder. Then the prediction is generated by the collaborative filtering algorithm. Experiments on MovieLens dataset show that the new algorithm can mine the potential features of item genres comprehensively, and improve the recommendation accuracy.

關鍵字

Denoising Autoencoder ； Collaborative Filtering ； Items with Same Genres ； User Preference

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