IFUP: Workshop on Multi-dimensional Information Fusion for User Modeling and Personalization

IFUP: Workshop on Multi-dimensional Information Fusion for User Modeling and Personalization

Zhu, Feida (Singapore Management University)
Zhang, Yongfeng (Rutgers University)
Yorke-Smith, N. (TU Delft Algorithmics; American University of Beirut)
Guo, Guibing (Northeastern University China)
Chen, Xu (National Tsing Hua University)

2018

Recommendation system has became an important component in many real applications, ranging from e-commerce, music app to video-sharing site and on-line book store. The key of a successful recommendation system lies in the accurate user/item profiling. With the advent of web 2.0, quite a lot of multimodal information has been accumulated, which provides us with the opportunity to profile users in a more comprehensive manner. However, directly integrating multimodal information into recommendation system is not a trivial task, because they may be either homogenous or heterogeneous, which requires more advanced method for both fusion and alignment. This workshop aims to provide a platform for discussing the challenges and corresponding innovative approaches in fusing multi-dimensional information for user modeling and recommender systems. We hope more advanced technologies can be proposed or inspired, and also we hope that the direction of integrating different types of information can catch much more attention in both academic and industry.

Information fusion
Multi-dimensional
User modeling

http://resolver.tudelft.nl/uuid:8f4a968b-f6a9-4083-a4e0-bc977b47f79d

https://doi.org/10.1145/3159652.3160592

804-805

WSDM 2018, 2018-02-06 → 2018-02-09, Los Angeles, CA, United States

Institutional Repository

abstract

© 2018 Feida Zhu, Yongfeng Zhang, N. Yorke-Smith, Guibing Guo, Xu Chen