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.

Submission Guidelines

Two page extended abstract reporting original results, preliminary results, and proposals for new work, will be considered for presentation/poster session at the workshop. We welcome research that has been previously published or is under review elsewhere. Manuscripts must be self-contained and in English with 2 pages in length plus references. Papers must be submitted in PDF according to the new ACM guidelines (e.g., using the ACM LaTeX template on Overleaf here). The PDF files must have all non-standard fonts embedded. After uploading your submission, please verify the copy stored on the EasyChair site. Please follow other guidelines for formatting submissions as indicated in the main conference page: KDD

At least one author of each accepted paper must attend the workshop and present the paper. The deadline for paper submission is 1st of June 2025. 

List of Topics

• Use of generative AI and LLMs for personalization of search systems
• Personalizations and recommendations for conversational assistants
• Personalized query interpretation and intent disambiguation
• Incorporating user context in real-time or in-session adaptation for personalization in search
• Using the user’s search session history for personalization and recommendation
• Personalized evidence generation and explainability of search results
• Fairness and privacy in personalized search
• Latency, caching, and other infrastructural considerations for real-world personalized search systems
• Learnings and challenges for developing large-scale personalized search systems
• Leveraging different modalities of data, including text, image, and audio, for the personalization of search
• New learning to rank (LTR) approaches for search personalization, as well as beyond traditional LTR approaches, such as reinforcement learning for search personalization
• Joint modeling of search and recommendation
• New applications for personalization and recommendations for search(e.g., in healthcare)
• Evaluation metrics for personalization and recommendation in search
• Optimization of delayed user behavioral rewards for personalization and recommendations in the context of search• Human-computer interaction considerations in designing personalized Search user interfaces and interaction
• Infrastructure for sparse and dense retrieval for a personalized Search and Recommendations experience

Committees

Program Committee

• Qingyao Ai (Tsinghua University)
• Yongfeng Zhang (Rutgers University)
• Maarten de Rijke (University of Amsterdam)
• Changsang Kang (Walmart)
• Chihoon Lee (Facebook)
• Alex Cozzi (EBay)
• Georges-Eric Dupret (Apple) 
• Fabrizio Silvestri (Sapienza Università di Roma)
• Roger Luo (Niantic)
• Liangjie Hong (LinkedIn)
• Yan Jiao (Tinder)
• Narayanan Sadagopan (Amazon Sciences)
• Edgar Meij (Bloomberg)
• Leo Boytsov (Bosch AI)
• Diane Hu (Etsy)
• Emine Yilmaz (UCL)

Organizing committee

• Sudarshan Lamkhede, Netflix Research  
• Moumita Bhattacharya, Netflix Research 

Contact

All questions about submissions should be emailed to paris-workshop at googlegroups dot com