Hybrid Collaborative Recommendation via Semi-AutoEncoder
Author(s)
Zhang, S
Yao, L
Xu, X
Wang, S
Zhu, L
Griffith University Author(s)
Year published
2017
Metadata
Show full item recordAbstract
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.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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Journal Title
Lecture Notes in Computer Science
Volume
10634
Subject
Other information and computing sciences not elsewhere classified