Editorial: Data mining for understanding user needs

Data mining and data analysis have a long history in human-computer interaction, starting with early interests in tracking the users then trying to infer models of users for adaptive systems [Benyon and Murray 1993; Fischer 1993], to more recent interests in attentional user interfaces, notifier systems, and recommenders. Recommender systems have emerged as a research area meriting a conference series since 2007, while attentional UIs have been the subject of several special issues [Horvitz et al. 2003; McCrickard et al. 2003b]. The convergence of analytic techniques for establishing patterns and orders in large datasets—data mining—and using such analysis to improve the responsiveness, user fit, and functionality of interactive systems has not been explicitly synthesized even though it has been a persistent interest in HCI. This special issue is therefore timely in bringing the fields of data mining and HCI together, As technology has developed over the past few decades, vast amounts of data have been generated as a result of users’ interactions with a range of applications from e-commerce to social networking sites. Analyzing this data can help in understanding the users’ needs and evaluating the effectiveness of user interaction. In turn, this can be used to improve the interface and interaction design, determine more suitable content, and develop useful services targeted at individual users. Data mining, also known as knowledge discovery [Fayyad and Uthurusamy 1996], is the process of extracting valuable information from large amounts