Social-aware VR configuration recommendation via multi-feedback coupled tensor factorization

Hsu Chao Lai, Jiun Long Huang, Hong Han Shuai, Wang Chien Lee, De Nian Yang, Philip S. Yu

研究成果: Conference contribution同行評審

4 引文 斯高帕斯(Scopus)

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, MultiView 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.

原文English
主出版物標題CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management
發行者Association for Computing Machinery
頁面1773-1782
頁數10
ISBN(電子)9781450369763
DOIs
出版狀態Published - 11月 2019
事件28th ACM International Conference on Information and Knowledge Management, CIKM 2019 - Beijing, China
持續時間: 3 11月 20197 11月 2019

出版系列

名字International Conference on Information and Knowledge Management, Proceedings

Conference

Conference28th ACM International Conference on Information and Knowledge Management, CIKM 2019
國家/地區China
城市Beijing
期間3/11/197/11/19

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