File(s) under permanent embargo
Representation learning with depth and breadth for recommendation using multi-view data
conference contribution
posted on 2018-01-01, 00:00 authored by Xiaotian Han, Chuan Shi, Lei Zheng, Philip S Yu, Jianxin LiJianxin Li, Yuanfu LuRecommender system has been well investigated in the past years. However, the typical representative CF-like models often give recommendation with low accuracy when the interaction information between users and items are sparse. To address the practical issue, in this paper we develop a novel Representation Learning with Depth and Breadth (RLDB) model for better recommendation Specifically, we design a heterogeneous network embedding method and convolutional neural network based method to learn feature representations of users and items from user-item interaction structure and review texts, respectively. Furthermore, an end-to-end breadth learning model is proposed through employing multi-view machine technique to learn features and fuse these diverse types of features in a uniform framework. Extensive experiments clearly demonstrates that our model outperforms all the other methods in these datasets.
History
Event
China Computer Federation. Conference (2nd : 2018 : Macau, China)Volume
10987Series
China Computer Federation ConferencePagination
181 - 188Publisher
SpringerLocation
Macau, ChinaPlace of publication
Cham, SwitzerlandPublisher DOI
Start date
2018-07-23End date
2018-07-25ISBN-13
978-3-319-96890-2Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2018, Springer International Publishing AG, part of Springer NatureEditor/Contributor(s)
Y Cai, Y Ishikawa, J XuTitle of proceedings
APWeb-WAIM 2018 : Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big DataUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC