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 2024
- Submission deadline: 12th of May 2024
- Author notification: 19th of May 2024
- Workshop: 18th of July 2024
All deadlines are 11:59 pm, Anywhere on Earth (AoE).
Contact
paris-workshop at googlegroups dot com
Organizers
- Sudarshan Lamkhede, Netflix Research
- Moumita Bhattacharya, Netflix Research
- Hongning Wang, Dept. of Computer Science, Virginia University
- Hamed Zamani, Dept. of Computer Science, University of Massachusetts Amherst
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)
- Sudeep Das (DoorDash)
- Hiba Ahsan (Northeastern University)
- Anlei Dong (Microsoft)