Item anomaly detection based on dynamic partition for time series in recommender systems
- Submitting institution
-
University of Portsmouth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 7110871
- Type
- D - Journal article
- DOI
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10.1371/journal.pone.0135155
- Title of journal
- PLoS One
- Article number
- e0135155
- First page
- -
- Volume
- 10
- Issue
- 8
- ISSN
- 1932-6203
- Open access status
- Out of scope for open access requirements
- Month of publication
- August
- Year of publication
- 2015
- 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
-
5
- Research group(s)
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B - Computational Intelligence
- Citation count
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper demonstrated a new approach to detecting abnormal item in Shilling attacks using time series partition and abnormal interval detection based on Chi-square distribution. This work is acknowledged and appreciated in more recent research work (Gao et al, 2020, Advances in Intelligent Systems and Computing; Yuan et al 2019, MLBDBI). This work was the foundation for Chala’s work in detecting shilling attacks (Chala, 2020, Physics and Engineering;) and it has been used to benchmark the performance of the shilling attack detection method proposed in Zhang’s work (Zhang et al, 2020, IEEE Transactions on Computational Social Systems).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -