TY - ABST
T1 - ComplexRec 2020
T2 - 14th ACM Conference on Recommender Systems, RecSys 2020
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 - Recommender Systems
KW - Personalized Interaction
KW - Fine-Grained User Signals
KW - Domain-Specific Constraints
KW - Composite Item Recommendations
KW - Recommender Systems
KW - Personalized Interaction
KW - Fine-Grained User Signals
KW - Domain-Specific Constraints
KW - Composite Item Recommendations
U2 - 10.1145/3383313.3411535
DO - 10.1145/3383313.3411535
M3 - Conference abstract in proceedings
AN - SCOPUS:85092733188
T3 - RecSys 2020 - 14th ACM Conference on Recommender Systems
SP - 609
EP - 610
BT - RecSys '20: Proceedings of the 14th ACM Conference on Recommender Systems
PB - Association for Computing Machinery
Y2 - 22 September 2020 through 26 September 2020
ER -