Special issue on learning from user interactions

Bendersky, M., Wang, X., Metzler, D., & Najork, M. (2017). Learning from user interactions in personal search via attribute parameterization. In Proceedings of the tenth ACM international conference on web search and data mining (pp. 791–799). Glowacka, D., Ruotsalo, T., Konuyshkova, K., Athukorala, K., Kaski, S., & Jacucci, G. (2013). Directing exploratory search: Reinforcement learning from user interactions with keywords. In Proceedings of the 2013 international conference on Intelligent user interfaces (pp. 117-128). Guo, Q., & Agichtein, E. (2012). Beyond dwell time: Estimating document relevance from cursor movements and other post-click searcher behavior. In Proceedings of the 21st international conference on World Wide Web (pp. 569–578). Hassan Awadallah, A., White, R. W., Pantel, P., Dumais, S. T., & Wang, Y. M. (2014). Supporting complex search tasks. In Proceedings of the 23rd ACM international conference on conference on information and knowledge management (pp. 829–838). Jagerman, R., Oosterhuis, H., & de Rijke, M. (2019). To model or to intervene: A comparison of counterfactual and online learning to rank from user interactions. In Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval (pp. 15–24). Kiseleva, J., Williams, K., Hassan Awadallah, A., Crook, A. C., Zitouni, I., & Anastasakos, T. (2016). Predicting user satisfaction with intelligent assistants. In Proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval (pp. 45–54). Liu, C., Liu, J., & Belkin, N. J. (2014). Predicting search task difficulty at different search stages. In Proceedings of the 23rd ACM international conference on conference on information and knowledge management (pp. 569–578). Liu, J., Mitsui, M., Belkin, N. J., & Shah, C. (2019). Task, information seeking intentions, and user behavior: Toward a multi-level understanding of Web search. In Proceedings of the 2019 conference on human information interaction and retrieval (pp. 123–132). Mehrotra, R. (2018). Inferring user needs and tasks from user interactions. Doctoral dissertation, UCL (University College London). Mehrotra, R., Awadallah, A. H., Kholy, A. E., & Zitouni, I. (2017a). Hey Cortana! Exploring the use cases of a desktop based digital assistant. In SIGIR 1st international workshop on conversational approaches to information retrieval (CAIR’17) (Vol. 4). Mehrotra, R., Awadallah, A. H., Shokouhi, M., Yilmaz, E., Zitouni, I., El Kholy, A., & Khabsa, M. (2017b). Deep sequential models for task satisfaction prediction. In Proceedings of the 2017 ACM on conference on information and knowledge management (pp. 737–746). Mehrotra, R., Awadallah, A. H., & Yilmaz, E. (2018). Learnir: Wsdm 2018 workshop on learning from user interactions. In Proceedings of the eleventh ACM international conference on web search and data mining (pp. 797–798). Mehrotra, R., Bhattacharya, P., & Yilmaz, E. (2016a). Uncovering task based behavioral heterogeneities in online search behavior. In Proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval (pp. 1049–1052). Mehrotra, R., Bhattacharya, P., & Yilmaz, E. (2016b). Deconstructing complex search tasks: A bayesian nonparametric approach for extracting sub-tasks. In Proceedings of the 2016 conference of the north american chapter of the association for computational linguistics: Human language technologies (pp. 599–605). Mehrotra, R., Bhattacharya, P., & Yilmaz, E. (2016c). Characterizing users’ multi-tasking behavior in web search. In Proceedings of the 2016 ACM on conference on human information interaction and retrieval (pp. 297–300). Mehrotra, R., Lalmas, M., Kenney, D., Lim-Meng, T., & Hashemian, G. (2019). Jointly leveraging intent and interaction signals to predict user satisfaction with slate recommendations. In The World Wide Web conference (pp. 1256–1267). Mehrotra, R., & Yilmaz, E. (2015). Terms, topics & tasks: Enhanced user modelling for better personalization. In Proceedings of the 2015 international conference on the theory of information retrieval (pp. 131–140). Mehrotra, R., & Yilmaz, E. (2017a). Extracting hierarchies of search tasks & subtasks via a bayesian nonparametric approach. In Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval (pp. 285–294). Mehrotra, R., & Yilmaz, E. (2017b). Task embeddings: Learning query embeddings using task context. In Proceedings of the 2017 ACM on conference on information and knowledge management (pp. 2199–2202). Mehrotra, R., Zitouni, I., Hassan Awadallah, A., Kholy, A. E., & Khabsa, M. (2017c). User interaction sequences for search satisfaction prediction. In Proceedings of the 40th International ACM SIGIR conference on research and development in information retrieval (pp. 165–174). Santy, S., Zulfikar, W., Mehrotra, R., & Yilmaz, E. (2019). Towards task understanding in visual settings. In Proceedings of the AAAI conference on artificial intelligence (Vol. 33, pp. 10027–10028). Verma, M., Yilmaz, E., Mehrotra, R., Kanoulas, E., Carterette, B., Craswell, N., & Bailey, P. (2016). Overview of the TREC tasks track 2016. In TREC. White, R. W., Richardson, M., & Yih, W. T. (2015). Questions vs. queries in informational search tasks. In Proceedings of the 24th international conference on World Wide Web (pp. 135–136).