TY - CHAP
T1 - ComplexRec 2020: Workshop on Recommendation in Complex Environments
AU - Bogers, Toine
AU - Koolen, Marijn
AU - Petersen, Casper
AU - Mobasher, Bamshad
AU - Tuzhilin, Alexander
PY - 2020/9/22
Y1 - 2020/9/22
N2 - During the past decade, recommender systems have rapidly become an indispensable element of websites, apps, and other platforms that are looking to provide personalized interaction to their users. As recommendation technologies are applied to an ever-growing array of non-standard problems and scenarios, researchers and practitioners are also increasingly faced with challenges of dealing with greater variety and complexity in the inputs to those recommender systems. For example, there has been more reliance on fine-grained user signals as inputs rather than simple ratings or likes. Many applications also require more complex domain-specific constraints on inputs to the recommender systems. The outputs of recommender systems are also moving towards more complex composite items, such as package or sequence recommendations. This increasing complexity requires smarter recommender algorithms that can deal with this diversity in inputs and outputs. The ComplexRec workshop series offers an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all solution.
AB - During the past decade, recommender systems have rapidly become an indispensable element of websites, apps, and other platforms that are looking to provide personalized interaction to their users. As recommendation technologies are applied to an ever-growing array of non-standard problems and scenarios, researchers and practitioners are also increasingly faced with challenges of dealing with greater variety and complexity in the inputs to those recommender systems. For example, there has been more reliance on fine-grained user signals as inputs rather than simple ratings or likes. Many applications also require more complex domain-specific constraints on inputs to the recommender systems. The outputs of recommender systems are also moving towards more complex composite items, such as package or sequence recommendations. This increasing complexity requires smarter recommender algorithms that can deal with this diversity in inputs and outputs. The ComplexRec workshop series offers an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all solution.
KW - Complex recommendation
UR - https://www.mendeley.com/catalogue/2450e283-43b5-3a19-ad12-e78809b21b95/
U2 - 10.1145/3383313.3411535
DO - 10.1145/3383313.3411535
M3 - Chapter
SN - 9781450375832
T3 - RecSys 2020 - 14th ACM Conference on Recommender Systems
SP - 609
EP - 610
BT - RecSys 2020 - 14th ACM Conference on Recommender Systems
PB - Association for Computing Machinery, Inc
ER -