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Improving augmented reality using recommender systems

Published: 12 October 2013 Publication History

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 attention recently. AR adds virtual objects into a user's real-world environment enabling live interaction in three dimensions. Limited by the small display of AR devices, content selection is one of the key issues to improve user experience. In this paper, we present an aggregated random walk algorithm incorporating personal preferences, location information, and temporal information in a layered graph. By adaptively changing the graph edge weight and computing the rank score, the proposed AR recommender system predicts users' preferences and provides the most relevant recommendations with aggregated information.

References

[1]
G. Adomavicius, R. Sankaranarayanan, S. Sen, and A. Tuzhilin. Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems, 23(1):103--145, 2005.
[2]
G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6):734--749, 2005.
[3]
M. Allamanis, S. Scellato, and C. Mascolo. Evolution of a location-based online social network: analysis and models. In Proceedings of the 2012 ACM Internet Measurement Conference, pages 145{158. ACM, 2012.
[4]
R. T. Azuma et al. A survey of augmented reality. Presence-Teleoperators and Virtual Environments, 6(4):355--385, 1997.
[5]
R. M. Bell and Y. Koren. Improved neighborhood-based collaborative filtering. In KDD Cup and Workshop at the 13th ACM SIGKDD, 2007.
[6]
S. Benford and L. Fahl--en. A spatial model of interaction in large virtual environments. In Proceedings of the 3rd Conference on European Conference on Computer-Supported Cooperative Work, pages 109--124. Kluwer Academic Publishers, 1993.
[7]
S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30(1):107--117, 1998.
[8]
F. Ricci. Mobile recommender systems. Information Technology & Tourism, 12(3):205--231, 2010.
[9]
F. Ricci and Q. N. Nguyen. Acquiring and revising preferences in a critique-based mobile recommender system. Intelligent Systems, 22(3):22--29, 2007.
[10]
S. Shang, S. R. Kulkarni, P. W. Cu, and P. Hui. A random walk based model incorporating social information for recommendations. In 2012 International Workshop on Machine Learning and Signal Processing, pages 1--6. IEEE, 2012.
[11]
K. H. Tso-Sutter, L. B. Marinho, and L. Schmidt-Thieme. Tag-aware recommender systems by fusion of collaborative filtering algorithms. In Proceedings of the 2008 ACM Symposium on Applied computing, pages 1995--1999. ACM, 2008.
[12]
L. Xiang, Q. Yuan, S. Zhao, L. Chen, X. Zhang, Q. Yang, and J. Sun. Temporal recommendation on graphs via long-and short-term preference fusion. In Proceedings of the 16th ACM SIGKDD, pages 723--732. ACM, 2010.

Cited By

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  • (2023)User behavior modeling for AR personalized recommendations in spatial transitionsVirtual Reality10.1007/s10055-023-00852-627:4(3033-3050)Online publication date: 24-Oct-2023
  • (2022)Personalization services in art education environments: first survey results2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)10.1109/IISA56318.2022.9904365(1-8)Online publication date: 18-Jul-2022
  • (2022)XR Management Training Simulator supported by Content-Based scenario recommendation2022 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)10.1109/AIVR56993.2022.00021(104-108)Online publication date: Dec-2022
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    cover image ACM Conferences
    RecSys '13: Proceedings of the 7th ACM conference on Recommender systems
    October 2013
    516 pages
    ISBN:9781450324090
    DOI:10.1145/2507157
    • General Chairs:
    • Qiang Yang,
    • Irwin King,
    • Qing Li,
    • Program Chairs:
    • Pearl Pu,
    • George Karypis
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 12 October 2013

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    Author Tags

    1. augmented reality
    2. graph
    3. high-dimensional
    4. pagerank
    5. random walk
    6. recommender system

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    RecSys '13 Paper Acceptance Rate 32 of 136 submissions, 24%;
    Overall Acceptance Rate 254 of 1,295 submissions, 20%

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    Cited By

    View all
    • (2023)User behavior modeling for AR personalized recommendations in spatial transitionsVirtual Reality10.1007/s10055-023-00852-627:4(3033-3050)Online publication date: 24-Oct-2023
    • (2022)Personalization services in art education environments: first survey results2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)10.1109/IISA56318.2022.9904365(1-8)Online publication date: 18-Jul-2022
    • (2022)XR Management Training Simulator supported by Content-Based scenario recommendation2022 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)10.1109/AIVR56993.2022.00021(104-108)Online publication date: Dec-2022
    • (2021)A2W: Context-Aware Recommendation System for Mobile Augmented Reality Web BrowserProceedings of the 29th ACM International Conference on Multimedia10.1145/3474085.3475413(2447-2455)Online publication date: 17-Oct-2021
    • (2021)Latent Factor Modeling of Perceived Quality for Stereoscopic 3D Video Recommendation2021 International Conference on 3D Immersion (IC3D)10.1109/IC3D53758.2021.9687271(1-8)Online publication date: 8-Dec-2021
    • (2016)ReadMeProceedings of the 24th ACM international conference on Multimedia10.1145/2964284.2967233(312-316)Online publication date: 1-Oct-2016
    • (2015)A File Recommendation Model For Cloud Storage SystemsProceedings of the annual conference on Brazilian Symposium on Information Systems: Information Systems: A Computer Socio-Technical Perspective - Volume 110.5555/2814058.2814078(111-118)Online publication date: 26-May-2015
    • (2014)Enabling an augmented reality ecosystemProceedings of the 2014 workshop on Mobile augmented reality and robotic technology-based systems10.1145/2609829.2609835(41-46)Online publication date: 11-Jun-2014

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