Abstract
In this paper, we present a novel structure, Semi-AutoEncoder, based on AutoEncoder. We generalize it into a hybrid collaborative filtering model for rating prediction as well as personalized top-n recommendations. Experimental results on two real-world datasets demonstrate its state-of-the-art performances.
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APA
Zhang, S., Yao, L., Xu, X., Wang, S., & Zhu, L. (2017). Hybrid collaborative recommendation via Semi-AutoEncoder. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10634 LNCS, pp. 185–193). Springer Verlag. https://doi.org/10.1007/978-3-319-70087-8_20
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