Collaborative Location Recommendation by Integrating Multi-dimensional Contextual Information
- Submitting institution
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The University of Huddersfield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 16
- Type
- D - Journal article
- DOI
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10.1145/3134438
- Title of journal
- ACM Transactions on Internet Technology
- Article number
- 32
- First page
- 1
- Volume
- 18
- Issue
- 3
- ISSN
- 1533-5399
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- 7
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in an ERA2010 A-rated ACM Transactions journal, this paper introduces a novel Point-of-Interest recommendation approach based on tensor factorisation with users’ social constraints and spatial influence as regularisation terms. The results of this paper have led to new ideas in context-aware and personalised recommendation in the Internet of Things, including a national competitively-won research grant (2020-2022) funded by Australian Research Council (https://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/LP190100140).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -