Jinyoung (Jin) Kim is a data scientist, data enthusiast and data evangelist. He is currently a Director of Naver Search US and the Head of Data & Analytics (DnA) organization at Naver Search and Cue: (LLM-based search engine) from Naver, building state-of-the-art analytics and experimentation platforms and practices with talented data engineers and scientists. More about his work at Naver can be found at DnA tech blog.

Previously at Snap Inc., he has led various projects on online (A/B) and offline (label-based) metric development, causal inference in the space of social media search and recommendation. In Microsoft, he has tackled challenging modeling and measurement problems in the area of web (Bing), email (Outlook), conversational (Cortana) search.

He has published more than a dozen papers in the area of information retrieval and data mining, and recently published a book titled Hello, Data Science, which became a national bestseller (#1 in technology category) in Korea with critical acclaim. He also regularly hosts the Data Intelligence podcast.

He has taught tutorials in evaluation and measurement of online services in both Microsoft and SIGIR, and consulted various teams in Microsoft on metric and experiment design. He has also served in program committees and organized workshops in SIGIR, WSDM, WWW, ECIR conferences.

Selected Papers

• Practice and Challenges in Building a Business-oriented Search Engine Quality Metric
  Nuo Chen, Donghyun Park, Hyungae Park, Kijun Choi, Tetsuya Sakai. 2023. In Proceedings of the 46th Annual International ACM SIGIR (SIGIR).

• A Versatile Framework for Evaluating Ranked Lists in Terms of Group Fairness and Relevance
  Tetsuya Sakai, Jin Young Kim, Inho Kang. 2023. ACM Transactions on Information Systems (Volume 42, Issue 1).

• Understanding and Modeling Success in Email Search
  Jin Young Kim, Nick Craswell, Susan Dumais, Filip Radlinski, Fang Liu. 2017. In Proceedings of the 40th Annual International ACM Conference on Research and Development in Information Retrieval (SIGIR).

• Offline Evaluation for Information Retrieval
  Jin Young Kim, Emine Yilmaz and Paul Thomas. 2016. Foundations and Trends in Information Retrieval (in preparation).

• Explicit In Situ User Feedback for Web Search Results
  Jin Young Kim, Jaime Teevan and Nick Craswell. 2016. In Proceedings of the 39th Annual International ACM Conference on Research and Development in Information Retrieval (SIGIR).

• Toward Predicting the Outcome of an A/B Experiment for Search Relevance
  Lihong Li, Jin Young Kim, and Imed Zitouni. 2015. In Proceedings of the 8th ACM International Conference on Web Search and Data Mining (WSDM).

• Contextual and dimensional relevance judgments for reusable SERP-level evaluation
  Peter B. Golbus, Imed Zitouni, Jin Young Kim, Ahmed Hassan, and Fernando Diaz. 2014. In Proceedings of the 23rd international conference on World wide web (WWW).

• Understanding Web Search Interactions with Dynamic Results Generated using Implicit Feedback
  Jin Young Kim, Mark Cramer, Jaime Teevan, Dmitry Lagun. 2013. In Proceedings of the 22nd ACM Conference on Information and Knowledge Management (CIKM).

• Relevance Dimensions in Preference-Based IR Evaluation
  Jin Young Kim, Gabriella Kazai, Imed Zitouni. 2013. In Proceedings the 35th annual international ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR).

• Characterizing web content, user interests, and search behavior by reading level and topic
  Jin Young Kim, Kevyn Collins-Thompson, Paul N. Bennett, Susan T. Dumais. 2012. In Proceedings of 5th ACM International Conference on Web Search and Data Mining, Seattle, WA, USA (WSDM).

• A Field Relevance Model for Structured Document Retrieval
  Jin Young Kim and W. Bruce Croft. 2012. In Proceedings of the 34th European Conference on Information Retrieval (ECIR), Barcelona, Spain.

Selected Talks

• SIGIR Tutorial on IR Evaluation: Designing an End-to-End Offline Evaluation Pipeline
  Jin Young Kim and Emine Yilmaz