Representation learning with depth and breadth for recommendation using multi-view data

Xiaotian Han, Chuan Shi, Lei Zheng, Philip S. Yu, Jianxin Li, Yuanfu Lu

Research output: Chapter in Book/Conference paperConference paper

2 Citations (Scopus)

Abstract

Recommender 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.

Original languageEnglish
Title of host publicationWeb and Big Data: Second International Joint Conference, APWeb-WAIM 2018
Subtitle of host publicationMacau, China, July 23-25, 2018: Proceedings, Part II
EditorsYi Cai, Yoshiharu Ishikawa, Jianliang Xu
Place of PublicationCham, Switzerland
PublisherSpringer-Verlag London Ltd.
Pages181-188
Number of pages8
ISBN (Electronic)9783319968933
ISBN (Print)9783319968926
DOIs
Publication statusPublished - 2018
Event2nd Asia Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2018 - Macau, China
Duration: 23 Jul 201825 Jul 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10987 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd Asia Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2018
CountryChina
CityMacau
Period23/07/1825/07/18

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