Workshop on Recommendation in Complex Scenarios (ComplexRec 2017)

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

doi:10.1145/3109859.3109958

Workshop on Recommendation in Complex Scenarios (ComplexRec 2017)

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

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Communication

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
Title of host publication	RecSys '17 Proceedings of the Eleventh ACM Conference on Recommender Systems
Number of pages	2
Publisher	Association for Computing Machinery
Publication date	31 Aug 2017
Pages	380-381
ISBN (Electronic)	978-1-4503-4652-8
DOIs	https://doi.org/10.1145/3109859.3109958
Publication status	Published - 31 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/

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 recommendation scenarios
User background needs
Ratings prediction
Item ranking

Access to Document

10.1145/3109859.3109958

complexrec2017-overviewAccepted author manuscript, 295 KB

https://dl.acm.org/citation.cfm?id=3109958

Cite this

@inproceedings{b0a907f648f944f98c0e2f13506a6e70,

title = "Workshop on Recommendation in Complex Scenarios (ComplexRec 2017)",

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.",

keywords = "Recommendation algorithms, Complex recommendation scenarios, User background needs, Ratings prediction, Item ranking, Recommendation algorithms, Complex recommendation scenarios, User background needs, Ratings prediction, Item ranking",

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

year = "2017",

month = aug,

day = "31",

doi = "10.1145/3109859.3109958",

language = "English",

pages = "380--381",

booktitle = "RecSys '17 Proceedings of the Eleventh ACM Conference on Recommender Systems",

publisher = "Association for Computing Machinery",

address = "United States",

note = "RecSys 2017: 11th ACM Conference on Recommender Systems, RecSys 2017 ; Conference date: 27-08-2017 Through 31-08-2017",

url = "https://recsys.acm.org/recsys17/",

}

Bogers, T, Koolen, M, Mobasher, B, Said, A & Tuzhilin, A 2017, Workshop on Recommendation in Complex Scenarios (ComplexRec 2017). in RecSys '17 Proceedings of the Eleventh ACM Conference on Recommender Systems. Association for Computing Machinery, pp. 380-381, RecSys 2017: 11th ACM Conference on Recommender Systems, Como, Italy, 27/08/2017. https://doi.org/10.1145/3109859.3109958

Workshop on Recommendation in Complex Scenarios (ComplexRec 2017). / Bogers, Toine; Koolen, Marijn; Mobasher, Bamshad et al.
RecSys '17 Proceedings of the Eleventh ACM Conference on Recommender Systems. Association for Computing Machinery, 2017. p. 380-381.

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Communication

TY - GEN

T1 - Workshop on Recommendation in Complex Scenarios (ComplexRec 2017)

AU - Bogers, Toine

AU - Koolen, Marijn

AU - Mobasher, Bamshad

AU - Said, Alan

AU - Tuzhilin, Alexander

N1 - Conference code: 11

PY - 2017/8/31

Y1 - 2017/8/31

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 recommendation scenarios

KW - User background needs

KW - Ratings prediction

KW - Item ranking

KW - Recommendation algorithms

KW - Complex recommendation scenarios

KW - User background needs

KW - Ratings prediction

KW - Item ranking

U2 - 10.1145/3109859.3109958

DO - 10.1145/3109859.3109958

M3 - Article in proceedings

SP - 380

EP - 381

BT - RecSys '17 Proceedings of the Eleventh ACM Conference on Recommender Systems

PB - Association for Computing Machinery

T2 - RecSys 2017: 11th ACM Conference on Recommender Systems

Y2 - 27 August 2017 through 31 August 2017

ER -

Workshop on Recommendation in Complex Scenarios (ComplexRec 2017)

Abstract

Conference

Keywords

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Cite this