標題: Social-Aware VR Configuration Recommendation via Multi-Feedback Coupled Tensor Factorization
作者: Lai, Hsu-Chao
Shuai, Hong-Han
Yang, De-Nian
Huang, Jiun-Long
Lee, Wang-Chien
Yu, Philip S.
資訊工程學系
電機工程學系
Department of Computer Science
Department of Electrical and Computer Engineering
關鍵字: Virtual reality group shopping;configuration recommendation;Multi-View Display;coupled tensor factorization
公開日期: 1-Jan-2019
摘要: Recent technological advent in virtual reality (VR) has attracted a lot of attention to the VR shopping, which thus far is designed for a single user. In this paper, we envision the scenario of VR group shopping, where VR supports: 1) flexible display of items to address diverse personal preferences, and 2) convenient view switching between personal and group views to foster social interactions. We formulate the Multiview-Enabled Configuration Recommendation (MECR) problem to rank a set of displayed items for a VR shopping user. We design the Multiview-Enabled Configuration Ranking System (MEIRS) that first extracts discriminative features based on Marketing theories and then introduces a new coupled tensor factorization model to learn the representation of users, Multi-View Display (MVD) configurations, and multiple feedback with content features. Experimental results manifest that the proposed approach outperforms personalized recommendations and group recommendations by at least 30.8% in large-scale datasets and 63.3% in the user study in terms of hit ratio and mean average precision.
URI: http://dx.doi.org/10.1145/3357384.3357952
http://hdl.handle.net/11536/155014
ISBN: 978-1-4503-6976-3
DOI: 10.1145/3357384.3357952
期刊: PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19)
起始頁: 1773
結束頁: 1782
Appears in Collections:Conferences Paper