It has been long argued that personalizing search can be very helpful. In recent years, with proliferation of personal computing devices and large number of logged-in experiences, search has evolved to a stage with many different product scenarios where personalization plays a crucial role for relevance quality and user satisfaction. Though search context plays a big role in determining the relevance of a given result, the utility of a search system for its users can be further enhanced by providing personalized results as well as recommendations within the search context. A variety of solutions have been developed for search engines in e-commerce systems, streaming/media content providers, social network systems and even in web search systems for such tasks.
However, the research discussions around personalization and recommendation for search remain fragmented across different conferences and workshops. We feel that there is a strong need for bringing together researchers and practitioners working on these problems for a robust discussion and sharing of ideas.
This workshop aims for researchers and practitioners from both academia and industry to engage in the discussions of algorithmic and system challenges in search personalization and effectively recommending in search context. It will include but not limited to the topics such as evaluation, query assistance, retrieval, ranking, context modeling, benchmark data and system efficiency for search personalization and recommendations within search contexts, for which more effective and efficient solutions can be shared and discussed. We expect the workshop to be of interest to large audiences in the research community of information retrieval and machine learning.
Important Dates for PaRiS 2022 [Extended Deadlines]
- Paper Submission Deadline:
Dec 18, 2021Jan 14, 2022 (11: 59 P.M. PDT) - Acceptance notification:
Jan 20, 2022Jan 17, 2022 - Workshop date: Feb 25, 2022
All deadlines are 11:59 pm, anywhere in the world.
Contact
paris2022 at googlegroups dot com
Organizers
- Sudarshan Lamkhede, Netflix Research
- Anlei Dong, Microsoft
- Moumita Bhattacharya, Netflix Research
- Hongning Wang, Dept. of Computer Science, Virginia University
Program Committee
- Changsung Kang (Walmart)
- Chihoon Lee (Facebook)
- Alex Cozzi (EBay)
- Georges-Eric Dupret (Spotify)
- Roger Luo (Niantic)
- Liangjie Hong (LinkedIn)
- Yan Jiao (Tinder)
- Narayanan Sadagopan (Amazon Sciences)
- Edgar Meij (Bloomberg)