dc.contributor.author | Zhang, S | |
dc.contributor.author | Yao, L | |
dc.contributor.author | Xu, X | |
dc.contributor.author | Wang, S | |
dc.contributor.author | Zhu, L | |
dc.date.accessioned | 2018-03-05T01:39:20Z | |
dc.date.available | 2018-03-05T01:39:20Z | |
dc.date.issued | 2017 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.doi | 10.1007/978-3-319-70087-8_20 | |
dc.identifier.uri | http://hdl.handle.net/10072/370473 | |
dc.description.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. | |
dc.description.peerreviewed | Yes | |
dc.language | English | |
dc.language.iso | eng | |
dc.publisher | Springer | |
dc.relation.ispartofpagefrom | 185 | |
dc.relation.ispartofpageto | 193 | |
dc.relation.ispartofjournal | Lecture Notes in Computer Science | |
dc.relation.ispartofvolume | 10634 | |
dc.subject.fieldofresearch | Other information and computing sciences not elsewhere classified | |
dc.subject.fieldofresearchcode | 469999 | |
dc.title | Hybrid Collaborative Recommendation via Semi-AutoEncoder | |
dc.type | Journal article | |
dc.type.description | C1 - Articles | |
dc.type.code | C - Journal Articles | |
gro.hasfulltext | No Full Text | |
gro.griffith.author | Wang, Sen | |