In this chapter, I discuss various aspects of the interactive search process, including next-generation search experiences and emerging trends Section 3.1). I discuss various models of search interaction that have been developed for applications such as click prediction, satisfaction, and relevance (Section 3.2). I also enumerate some of the main components required for model building, including data, data mining, and machine learning (Section 3.3). From the breadth of the topics covered in this chapter, clear that many factors must be considered in modeling interests and intentions via interactions with search systems.
Modeling Next-Generation Search Interaction
Let us begin with a high-level model of next-generation search interaction that reflects emerging trends in the area, yet builds on much of the work on collecting and representing search interaction that was described in Chapter 2. The model is depicted visually in Figure 3.1. Although an interaction model is not strictly necessary for a discussion of progress in this area, it can be useful in framing many of the contributions that are mentioned in this book. I discuss emerging trends likely to affect search interaction, as well as other factors including the role of large-scale behavioral data in guiding effective decisions by future searchers and search providers; generic and personalized machine-learned models of searchers’ interests, intentions, and search satisfaction levels; support for task completion; cloud-based application and storage (used to retain found information items that were found, generated by the searcher, or in the process of being generated (work in progress), as well as rapidly accessible profiles of searchers’ long-term interests and intentions – reflecting completed and ongoing search tasks); and context of various forms, natural interaction with search systems, and ubiquitous search (through mobile computing and support for cross-device interactions). Core elements of next-generation search interactions are proactive and reactive experiences and intelligent personal assistants working in concert to surface relevant and useful information at an appropriate time.
There are a number of emerging trends in computer and information sciences, and in society more broadly, that search engine designers need to consider when designing next-generation search systems. These will influence the design of search technologies, how people interact with search systems, searcher experiences, and search system evaluation. Some of the main trends can be summarized as follows:
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