DOAJ Open Access 2021

Collaborative Filtering Recommendation Algorithm Based on Semi-Autoencoder

ZHANG Haobo, XUE Feng, LIU Kai

Lihat Sumber DOI

Abstrak

To effectively use the user-item interaction history and auxiliary information in recommendation systems,this paper proposes an improved collaborative filtering recommendation algorithm.Based on semi-autoencoder,the features of auxiliary information of users and items are extracted,and then mapped into the Matrix Factorization(MF) model.By using the back propagation algorithm,the semi-autoencoder and the matrix factorization model are jointly updated to improve the recommendation performance.Experimental results on the public datasets of MovieLens-100K and Book-Crossing show that the proposed algorithm provides better recommendation effects than the traditional recommendation algorithms,including the Biased Singular Value Decomposition(Biased SVD) and the Probabilistic Matrix Factorization(PMF) algorithm.

Topik & Kata Kunci

Computer engineering. Computer hardware Computer software

Penulis (1)

ZHANG Haobo, XUE Feng, LIU Kai

Format Sitasi

APA MLA BibTeX

Kai, Z.H.X.F.L. (2021). Collaborative Filtering Recommendation Algorithm Based on Semi-Autoencoder. https://doi.org/10.19678/j.issn.1000-3428.0056700

Akses Cepat

Lihat di Sumber doi.org/10.19678/j.issn.1000-3428.0056700

Informasi Jurnal

Tahun Terbit: 2021
Sumber Database: DOAJ
DOI: 10.19678/j.issn.1000-3428.0056700
Akses: Open Access ✓