Abstract
Recommendation algorithms for ratings prediction and item ranking have steadily matured during the past decade. However, these state-of-the-art algorithms are typically applied in relatively straightforward scenarios. In reality, recommendation is often a more complex problem: it is usually just a single step in the user's more complex background need. These background needs can often place a variety of constraints on which recommendations are interesting to the user and when they are appropriate. However, relatively little research has been done on these complex recommendation scenarios. The ComplexRec 2017 workshop addressed this by providing an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all-solution.
Originalsprog | Engelsk |
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Forlag | CEUR Workshop Proceedings |
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Antal sider | 28 |
Status | Udgivet - 27 aug. 2017 |
Udgivet eksternt | Ja |
Begivenhed | RecSys 2017: 11th ACM Conference on Recommender Systems - Como, Italy, Como, Italien Varighed: 27 aug. 2017 → 31 aug. 2017 Konferencens nummer: 11 https://recsys.acm.org/recsys17/ |
Navn | CEUR Workshop Proceedings |
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Vol/bind | 1892 |
ISSN | 1613-0073 |
Konference
Konference | RecSys 2017: 11th ACM Conference on Recommender Systems |
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Nummer | 11 |
Lokation | Como, Italy |
Land/Område | Italien |
By | Como |
Periode | 27/08/2017 → 31/08/2017 |
Internetadresse |
Emneord
- Recommendation Algorithms
- Complex Scenarios
- User Needs
- Constraints in Recommendations
- ComplexRec 2017