Postdoctoral researcher working on recommender systems, exploratory search, user experience and machine learning. Proven industry experience as software developer (3+ years). Competent in Java, Python, SQL, R.

Videos

Selected publications

  • Denis Kotkov, Alexandr Maslov, and Mats Neovius. 2021. Revisiting the Tag Relevance Prediction Problem. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’21), July 11–15, 2021, Virtual Event, Canada.ACM, New York, NY,USA, 5 pages 10.1145/3404835.3463019 Paper Video 
  • Denis Kotkov, Qian Zhao, Kati Launis, and Mats Neovius. 2020. ClusterExplorer: Enable User Control over Related Recommendations via Collaborative Filtering and Clustering. In Fourteenth ACM Conference on Recommender Systems (RecSys ’20), September 22–26, 2020, Virtual Event, Brazil. ACM, New York, NY, USA, 6 pages. 10.1145/3383313.3412221 Paper  Video  Demo
  • Denis Kotkov, Joseph A. Konstan, Qian Zhao, and Jari Veijalainen. Investigating serendipity in recommender systems based on real user feedback. In Proceedings of the 33rd Annual ACM Symposium on Applied Computing, pages 1341-1350, 2018. ACM. doi: 10.1145/3167132.3167276 Paper   Slides   Video
  • Denis Kotkov, Shuaiqiang Wang, and Jari Veijalainen. A survey of serendipity in recommender systems. Knowledge-Based Systems, 111:180-192, 2016. doi: 10.1016/j.knosys.2016.08.014 Paper

Publications

  1. Denis Kotkov, Alan Medlar, Triin Kask, and Dorota Głowacka. 2024. The Dark Matter of Serendipity in Recommender Systems. In Proceedings of the 2024 ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR ’24), March 10–14, 2024, Sheffield, United Kingdom. ACM, New York, NY, USA, 11 pages. 10.1145/3627508.3638342 Paper
  2. Denis Kotkov, Alan Medlar, and Dorota Glowacka. 2023. Rethinking Serendipity in Recommender Systems. In Proceedings of the 2023 Conference on Human Information Interaction and Retrieval, March, 2023, 2022, Austin, TX, USA. ACM, New York, NY, USA, 5 pages. 10.1145/3576840.3578310 Paper Video 
  3. Denis Kotkov, Alan Medlar, Umesh Raj Satyal, Alexandr Maslov, Mats Neovius, and Dorota Glowacka. 2022. Rating consistency is consistently underrated: An exploratory analysis of movie-tag rating inconsistency. In The 37th ACM/SIGAPP Symposium on Applied Computing (SAC ’22), April 25–29, 2022, Virtual Event. ACM, New York, NY, USA, 10 pages. 10.1145/3477314.3507270 Paper Video 
  4. Denis Kotkov, Alan Medlar, Alexandr Maslov, Umesh Raj Satyal, Mats Neovius, and Dorota Glowacka. 2022. The Tag Genome Dataset for Books. In Proceedings of the 2022 ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR ’22), March 14–18, 2022, Regensburg, Germany. ACM, New York, NY, USA, 5 pages. 10.1145/3498366.3505833 Paper Video 
  5. Denis Kotkov, Alexandr Maslov, and Mats Neovius. 2021. Revisiting the Tag Relevance Prediction Problem. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’21), July 11–15, 2021, Virtual Event, Canada.ACM, New York, NY,USA, 5 pages 10.1145/3404835.3463019 Paper Video 
  6. Denis Kotkov, Qian Zhao, Kati Launis, and Mats Neovius. 2020. ClusterExplorer: Enable User Control over Related Recommendations via Collaborative Filtering and Clustering. In Fourteenth ACM Conference on Recommender Systems (RecSys ’20), September 22–26, 2020, Virtual Event, Brazil. ACM, New York, NY, USA, 6 pages. 10.1145/3383313.3412221 Paper  Video  Demo
  7. Denis Kotkov, Kati Launis, and Mats Neovius. 2018. In quest of transition books. In Proceedings of the Digital Humanities in the Nordic Countries 5th Conference., pages 275–283, 2020 Paper
  8. Denis Kotkov, Jari Veijalainen, and Shuaiqiang Wang. How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm. Computing. 102.2 (2020): 393-411. doi: 10.1007/s00607-018-0687-5 Paper
  9. Gaurav Pandey, Denis Kotkov, and Alexander Semenov. 2018. Recommending Serendipitous Items using Transfer Learning. In The 27th ACM International Conference on Information and Knowledge Management (CIKM’18), October 22–26, 2018, Torino, Italy. ACM, New York, NY, USA, 4 pages. doi: 10.1145/3269206.3269268 Paper
  10. Denis Kotkov, Gaurav Pandey, and Alexander Semenov. Gaming Bot Detection: A Systematic Literature Review. In International Conference on Computational Social Networks. Springer, Cham, 2018. doi: 10.1007/978-3-030-04648-4_21 Paper
  11. Denis Kotkov, Joseph A. Konstan, Qian Zhao, and Jari Veijalainen. Investigating serendipity in recommender systems based on real user feedback. In Proceedings of the 33rd Annual ACM Symposium on Applied Computing, pages 1341-1350, 2018. ACM. doi: 10.1145/3167132.3167276 Paper   Slides   Video
  12. Denis Kotkov, Shuaiqiang Wang, and Jari Veijalainen. Improving Serendipity and Accuracy in Cross-Domain Recommender Systems. In International Conference on Web Information Systems and Technologies. Springer, Cham, 2017. doi: 978-3-319-66468-2_6 Paper
  13. Denis Kotkov, Jari Veijalainen, and Shuaiqiang Wang. A serendipity-oriented greedy algorithm for recommendations. In Proceedings of the 13th International conference on web information systems and technologies. SCITEPRESS, 2017. doi: 10.5220/0006232800320040 Paper
  14. Boyang Zhang, Jari Veijalainen, and Denis Kotkov. Samsung and Volkswagen Crisis Communication in Facebook and Twitter - A Comparative Study. In Proceedings of the 13th International conference on web information systems and technologies. SCITEPRESS, 2017. doi: 10.5220/0006301403120323 Paper
  15. Denis Kotkov, Shuaiqiang Wang, and Jari Veijalainen. A survey of serendipity in recommender systems. Knowledge-Based Systems, 111:180-192, 2016. doi: 10.1016/j.knosys.2016.08.014 Paper
  16. Denis Kotkov, Jari Veijalainen, and Shuaiqiang Wang. Challenges of serendipity in recommender systems. In Proceedings of the 12th International Conference on Web Information Systems and Technologies, volume 2, pages 251-256. SCITEPRESS, 2016. doi: 10.5220/0005879802510256 Paper
  17. Denis Kotkov, Shuaiqiang Wang, and Jari Veijalainen. Cross-domain recommendations with overlapping items. In Proceedings of the 12th International conference on web information systems and technologies, volume 2, pages 131-138. SCITEPRESS, 2016. doi: 10.5220/0005851301310138 Paper
  18. Boyang Zhang, Denis Kotkov, Jari Veijalainen, and Alexander Semenov. Online Stakeholder Interaction of Some Airlines in the Light of Situational Crisis Communication Theory. In Conference on e-Business, e-Services and eSociety, pages 183–192. Springer, 2016. doi: 10.1007/978-3-319-45234-0_17
  19. Boyang Zhang, Vos Marita, Jari Veijalainen, Shuaiqiang Wang, and Denis Kotkov. The issue arena of a corporate social responsibility crisis-the volkswagen case in twitter. Studies in Media and Communication, 4(2):32–43, 2016. doi: 10.11114/smc.v4i2.1746 Paper
  20. Boyang Zhang, Jari Veijalainen, and Denis Kotkov. Volkswagen emission crisis: Managing stakeholder relations on the web. In Proceedings of the 12th International Conference on Web Information Systems and Technologies, volume 1, pages 176-187. SCITEPRESS, 2016. doi: 10.5220/0005892401760187 Paper
  21. Aleksandr Farseev, Denis Kotkov, Alexander Semenov, Jari Veijalainen, and Tat-Seng Chua. Cross-social network collaborative recommendation. In Proceedings of the ACM Web Science Conference. ACM, 2015. doi: 10.1145/2786451.2786504 Paper

Teaching

Teaching at the University of Helsinki, Finland

  • Topics in Recommender Systems (2023) covers offline evaluation, collaborative filtering, content-based filtering, hybrid algorithms, deep learning, non-personalized algorithms and online experiments. The course includes hands on assignments. Tasks: designing the course based on recent literature, delivering lectures, designing and grading programming assignments.

Teaching assistance at the University of Helsinki, Finland

Teaching assistance at the Åbo Akademi University, Finland

  • Software Testing (2020) on testing process from requirements to deployment. It includes topics, such as coverage criteria, input space partitioning, syntax-based testing, regression testing, unit testing and integration testing, model-based testing. Practical exercises are completed in Java. Tasks: teaching lab sessions, preparing and grading assignments

Teaching assistance at the University of Jyväskylä, Finland