Special issue on learning from user interactions
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ecial issue on learning from user interactions Rishabh Mehrotra1 · Ahmed Hassan Awadallah2 · Emine Yilmaz3 Received: 7 October 2020 / Accepted: 8 October 2020 / Published online: 24 October 2020 © Springer Nature B.V. 2020
When users interact with online services (e.g. search engines, recommender systems, conversational agents), they leave behind traces of interaction patterns. The ability to record and interpret user interaction signals (Guo and Agichtein 2012) and understand user behavior (Mehrotra et al. 2016a) gives online systems a vast treasure trove of insights for improvement and experimentation. More generally, the ability to learn from user interactions promises pathways for solving a number of problems such as improving user engagement, incorporating user feedback and gauging user satisfaction. Understanding and learning from user interactions involves a number of different aspects (Mehrotra et al. 2018; Mehrotra 2018)—from understanding user intent and tasks (Mehrotra et al. 2016b, c; Mehrotra and Yilmaz 2017a; Santy et al. 2019; White et al. 2015), to developing user models and personalization services (Mehrotra and Yilmaz 2015, 2017; Liu et al. 2019). Beyond understanding user needs, learning from user interactions involves developing the right metrics for evaluation and experimentation systems (Mehrotra et al. 2017b, c, Verma et al. 2016), understanding user interaction processes (Liu et al. 2014), their usage context (Mehrotra and Yilmaz 2017b) and designing interfaces capable of helping users (Hassan Awadallah et al. 2014). As such, understanding user behavior could allow the system to support users at the various stages of their tasks. This could have implications on many aspects of the system design including user interface, understanding user intents in search and recommendation scenarios (Mehrotra et al. 2019), presentation of information, retrieving and ranking (Bendersky et al. 2017; Glowacka et al. 2013), unbiased learning (Jagerman et al. 2019), etc. Learning from user interactions becomes more important as new and novel ways of user interactions surface. There is a gradual shift towards searching and presenting the information in a conversational form Kiseleva et al. (2016), Mehrotra et al. (2017a). Chatbots, personal assistants on mobile phones, smart speakers and other eyes-free devices are being used increasingly more for different purposes, including information retrieval and exploration. With improved speech recognition and information retrieval systems, more and more
* Rishabh Mehrotra [email protected] 1
Spotify Research, London, UK
2
Microsoft Research, Redmond, USA
3
University College London, London, UK
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Vol.:(0123456789)
526
Information Retrieval Journal (2020) 23:525–527
users are increasingly relying on such digital assistants to fulfil their information needs and complete their tasks. Such systems rely heavily on quickly learning from past interactions and incorporating implicit feedback signals into their models.
1 Overview of articles in this special
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