ComplexRec 2018: Proceedings of Second Workshop on Recommendation in Complex Scenarios

Casper Petersen; Toine Bogers; Marijn Koolen; Bamshad Mobasher; Alan Said

ComplexRec 2018: Proceedings of Second Workshop on Recommendation in Complex Scenarios

Casper Petersen (Editor), Toine Bogers (Editor), Marijn Koolen (Editor), Bamshad Mobasher (Editor), Alan Said (Editor)

Department of Communication and Psychology

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

Abstract

Over the past decade, recommendation algorithms for ratings prediction and item ranking have steadily matured. However, these state-of-the-art algorithms are typically applied in relatively straightforward and static scenarios: given information about a user’s past item preferences in isolation, can we predict whether they will like a new item or rank all unseen items based on predicted interest?

In reality, recommendation is often a more complex problem: the evaluation of a list of recommended items never takes place in a vacuum, and it is often a single step in the user’s more complex background task or need. The goal of the ComplexRec 2018 workshop is to offer an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all solution.

Original language	English

Number of pages	37
Publication status	Published - 7 Oct 2018
Event	ComplexRec 2018: 2nd Workshop on Recommendation in Complex Scenarios - Vancouver, BC, Canada, Vancouver, Canada Duration: 7 Oct 2018 → 7 Oct 2018 Conference number: 2 http://complexrec2018.aau.dk

Workshop

Workshop	ComplexRec 2018: 2nd Workshop on Recommendation in Complex Scenarios
Number	2
Location	Vancouver, BC, Canada
Country/Territory	Canada
City	Vancouver
Period	07/10/2018 → 07/10/2018
Internet address	http://complexrec2018.aau.dk

Keywords

complex recommendation
recommender systems

Access to Document

http://toinebogers.com/workshops/complexrec2018/resources/proceedings.pdf

AUB Link

Search for the material in Aalborg University Library's search engine

1 Conference organisation or participation
1 Organisation or participation in workshops, courses, seminars, exhibitions or similar

RecSys 2018: 12th ACM Conference on Recommender Systems
Toine Bogers (Participant)
2 Oct 2018 → 7 Oct 2018
Activity: Attending an event › Conference organisation or participation
ComplexRec 2018: 2nd Workshop on Recommendation in Complex Scenarios
Toine Bogers (Organizer), Marijn Koolen (Organizer), Bamshad Mobasher (Organizer), Casper Petersen (Organizer) & Alan Said (Organizer)
2 Oct 2018
Activity: Attending an event › Organisation or participation in workshops, courses, seminars, exhibitions or similar

1 Article in proceeding

2nd workshop on recommendation in complex scenarios (ComplexreC 2018)
Bogers, A. M., Koolen, M., Mobasher, B., Said, A. & Petersen, C., 27 Sept 2018, RecSys 2018 - 12th ACM Conference on Recommender Systems. Association for Computing Machinery, p. 510-511 2 p.
Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review
5 Citations (Scopus)

Cite this

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title = "ComplexRec 2018: Proceedings of Second Workshop on Recommendation in Complex Scenarios",

abstract = "Over the past decade, recommendation algorithms for ratings prediction and item ranking have steadily matured. However, these state-of-the-art algorithms are typically applied in relatively straightforward and static scenarios: given information about a user{\textquoteright}s past item preferences in isolation, can we predict whether they will like a new item or rank all unseen items based on predicted interest?In reality, recommendation is often a more complex problem: the evaluation of a list of recommended items never takes place in a vacuum, and it is often a single step in the user{\textquoteright}s more complex background task or need. The goal of the ComplexRec 2018 workshop is to offer an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all solution.",

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N1 - Conference code: 2

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N2 - Over the past decade, recommendation algorithms for ratings prediction and item ranking have steadily matured. However, these state-of-the-art algorithms are typically applied in relatively straightforward and static scenarios: given information about a user’s past item preferences in isolation, can we predict whether they will like a new item or rank all unseen items based on predicted interest?In reality, recommendation is often a more complex problem: the evaluation of a list of recommended items never takes place in a vacuum, and it is often a single step in the user’s more complex background task or need. The goal of the ComplexRec 2018 workshop is to offer an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all solution.

AB - Over the past decade, recommendation algorithms for ratings prediction and item ranking have steadily matured. However, these state-of-the-art algorithms are typically applied in relatively straightforward and static scenarios: given information about a user’s past item preferences in isolation, can we predict whether they will like a new item or rank all unseen items based on predicted interest?In reality, recommendation is often a more complex problem: the evaluation of a list of recommended items never takes place in a vacuum, and it is often a single step in the user’s more complex background task or need. The goal of the ComplexRec 2018 workshop is to offer an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all solution.

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ER -

ComplexRec 2018: Proceedings of Second Workshop on Recommendation in Complex Scenarios

Abstract

Workshop

Keywords

Access to Document

AUB Link

Fingerprint

Activities

RecSys 2018: 12th ACM Conference on Recommender Systems

ComplexRec 2018: 2nd Workshop on Recommendation in Complex Scenarios

Research output

2nd workshop on recommendation in complex scenarios (ComplexreC 2018)

Cite this