Personalized Recommendation Considering Secondary Implicit Feedback
- Submitting institution
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Swansea University / Prifysgol Abertawe
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 49645
- Type
- E - Conference contribution
- DOI
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10.1109/agents.2018.8460053
- Title of conference / published proceedings
- 2018 IEEE International Conference on Agents (ICA)
- First page
- 87
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- July
- Year of publication
- 2018
- URL
-
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
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- Research group(s)
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- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper addresses the problem of recommending items that are different from users’ previous purchase history in e-commerce platform by considering secondary implicit feedback, e.g., e.g., viewing items, adding items to shopping cart, adding items to favourite list, etc. Experiments with a large-scale real-world e-commerce dataset show that the work presents a superior performance in comparison with the state-of-the-art baselines in terms of recommendation accuracy even when there is serious data sparsity. This paper won the best paper award in IEEE International Conference on Agents (ICA) in 2018.
- Author contribution statement
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- Non-English
- No
- English abstract
- -