Improving augmented reality using recommender systems

Zhuo Zhang, Shang Shang, Sanjeev R. Kulkarni, Pan Hui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

With the rapid development of smart devices and wireless communication, especially with the pre-launch of Google Glass, augmented reality (AR) has received enormous at- tention recently. AR adds virtual objects into a user's real- world environment enabling live interaction in three dimen- sions. Limited by the small display of AR devices, content selection is one of the key issues to improve user experi- ence. In this paper, we present an aggregated random walk algorithm incorporating personal preferences, location infor- mation, and temporal information in a layered graph. By adaptively changing the graph edge weight and computing the rank score, the proposed AR recommender system pre- dicts users' preferences and provides the most relevant rec- ommendations with aggregated informatio.

Original languageEnglish (US)
Title of host publicationRecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems
Pages173-176
Number of pages4
DOIs
StatePublished - 2013
Event7th ACM Conference on Recommender Systems, RecSys 2013 - Hong Kong, China
Duration: Oct 12 2013Oct 16 2013

Publication series

NameRecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems

Other

Other7th ACM Conference on Recommender Systems, RecSys 2013
Country/TerritoryChina
CityHong Kong
Period10/12/1310/16/13

All Science Journal Classification (ASJC) codes

  • Software

Keywords

  • Augmented reality
  • Graph
  • High-dimensional
  • PageRank
  • Random walk
  • Recommender system

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