ComplexRec 2017: Recommendation in Complex Scenarios

Toine Bogers; Marijn Koolen; Bamshad Mobasher; Alan Said; Alexander Tuzhilin

ComplexRec 2017: Recommendation in Complex Scenarios

Toine Bogers (Editor), Marijn Koolen (Editor), Bamshad Mobasher (Editor), Alan Said (Editor), Alexander Tuzhilin (Editor)

Research output: Book / Anthology / Report › Anthology › Research › peer-review

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.

Original language	English

Publisher	CEUR Workshop Proceedings
Number of pages	28
Publication status	Published - 27 Aug 2017
Externally published	Yes
Event	RecSys 2017: 11th ACM Conference on Recommender Systems - Como, Italy, Como, Italy Duration: 27 Aug 2017 → 31 Aug 2017 Conference number: 11 https://recsys.acm.org/recsys17/

Series	CEUR Workshop Proceedings
Volume	1892
ISSN	1613-0073

Conference

Conference	RecSys 2017: 11th ACM Conference on Recommender Systems
Number	11
Location	Como, Italy
Country/Territory	Italy
City	Como
Period	27/08/2017 → 31/08/2017
Internet address	https://recsys.acm.org/recsys17/

Keywords

Recommendation Algorithms
Complex Scenarios
User Needs
Constraints in Recommendations
ComplexRec 2017

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http://ceur-ws.org/Vol-1892/

Cite this

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title = "ComplexRec 2017: Recommendation in Complex Scenarios",

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.",

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editor = "Toine Bogers and Marijn Koolen and Bamshad Mobasher and Alan Said and Alexander Tuzhilin",

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N2 - 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.

AB - 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.

KW - Recommendation Algorithms

KW - Complex Scenarios

KW - User Needs

KW - Constraints in Recommendations

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KW - Complex Scenarios

KW - User Needs

KW - Constraints in Recommendations

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Y2 - 27 August 2017 through 31 August 2017

ER -

ComplexRec 2017: Recommendation in Complex Scenarios

Abstract

Conference

Keywords

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