User modeling in adaptive interfaces

In this paper we examine the notion of adaptive user interfac s, interactive systems that invoke machine learning to improve their interact ion with humans. We review some previous work in this emerging area, ranging from softw are that filters information to systems that support more complex tasks like scheduling. After this, we describe three ongoing research efforts that extend this framework in new d irections. Finally, we review previous work that has addressed similar issues and conside r some challenges that are presented by the design of adaptive user interfaces. 1 The Need for Automated User Modeling As computers have become more widespread, the software that runs on them has also become more interactive and responsive. Only a few early users remember the days of programming on punch cards and submitting overnight jobs, and even the era of time-s haring systems and text editors has become a dim memory. Modern operating systems support a wid e range of interactive software, from WYSIWYG editors to spreadsheets to computer games, most em bedded in some form of graphical user interface. Such packages have become an essential part of bus iness and academic life, with millions of people depending on them to accomplish thei r daily goals. Naturally, the increased emphasis on interactive software has led to greater in ter st in the study of human-computer interaction. However, most research in this area h s focused on the manner in which computer interfaces present information and choices to the user , and thus tells only part of the story. An equally important issue, yet one that has receiv d much less attention, concerns thecontentthat the interface offers to the user. And a concern with content leads directl y to a focus onuser models , since it seems likely that people will differ in the content they prefer to encounter during their interactions with computers. Developers of software for the Internet are quite aware of the need for p ersonalized content, and many established portals on the World Wide Web provide simple too s f r filtering information. But these tools typically focus on a narrow class of applications an d require manual setting of parameters, a process that users are likely to find tedious. Moreover, som e facets of users’ preferences may be reflected in their behavior but not subject to introspection . Clearly, there is a need for increased personalization in many areas of interactive software, both i n supporting a greater variety of tasks and in ways to automate this process. This suggests turning to techniques from machine learning in order to personalize computer interfaces. ? Also affiliated with the Institute for the Study of Learning a nd Expertise and the Center for the Study of Language and Information at Stanford University. 358 USERMODELING AND ADAPTIVE INTERFACES In the rest of this paper, we examine the notion of adaptive user interfaces – systems that learn a user model from traces of interaction with that user. We start by defining ad aptive interfaces more precisely, drawing a close analogy with algorithms for machine lear ning. Next, we consider some examples of such software artifacts that have appeared in the literatur e, af e which we report on three research efforts that attempt to extend the basic framework in new directions. Finally, we discuss kinships between adaptive user interfaces and some sim ilar paradigms, then close with some challenges they pose for researchers and software developer s. 2 Adaptive User Interfaces and Machine Learning For most readers, the basic idea of an adaptive user interface will already be cl ear, but for the sake of discussion, we should define this notion somewhat more preci sely: An adaptive user interface is a software artifact that improves its ability to interact with a user by constructing a user model based on partial experience with that user . This definition makes clear that an adaptive interface does not exist in isolat ion, but rather is designed to interact with a human user. Moreover, for the system to be adapt ive, it must improve its interaction with that user, and simple memorization of such interactio ns does not suffice. Rather, improvement should result from generalization over past experiences a d carry over to new user interactions. The above definition will seem familiar to some readers, and for good reason , since it takes the same form as common definitions of machine learning (e.g., Langley, 1 995). The main differences are that the user plays the role of the environment in which learning o ccurs, the user model takes the place of the learned knowledge base, and interaction with the user ser ves as the performance task on which learning should lead to improvement. In this view, adap tive user interfaces constitute a special class of learning systems that are designed to aid humans , in contrast with much of the early applied work on machine learning, which aimed to develop kn wledge-based systems that would replace domain experts. Despite this novel emphasis, many lessons acquired from these earlier appli cations of machine learning should prove relevant in the design of adaptive interfaces . The most important has been the realization that we are still far from entirely automating the lear ning process, and that some essential steps must still be done manually (Brodley and Smy th, 1997; Langley and Simon, 1995; Rudström, 1995). Briefly, to solve an applied probl em using established induction methods, the developer must typically: reformulate the problem in some form that these methods can directly addr ess; engineer a set of features that describe the training cases adequately; and devise some approach to collecting and preparing the training instances. Only after the developer has addressed these issues can he run some learning m ethod over the data to produce the desired domain knowledge or, in the case of an adaptive interface, the desired user model. Moreover, there is an emerging consensus within the applied learning commu nity that these steps of problem formulation, representation engineering, and data collect ion/preparation play a role at least as important as the induction stage itself. Indeed, there is a common belief that, once USERMODELING AND ADAPTIVE INTERFACES 359 they are handled well, the particular induction method one uses has littl e effect on the outcome (Langley and Simon, 1995). In contrast, most academic work on machine lear ning still focuses on refining induction techniques and downplays the steps that must occur b efore and after their invocation. Indeed, some research groups still emphasize differences between b road classes of learning methods, despite evidence that decision-tree induction, connect ionist algorithms, casebased methods, and probabilistic schemes often produce very similar resul ts. We will adopt the former viewpoint in our discussion of adaptive us r interfaces. As a result, we will have little to say about the particular learning methods used to construct and refine user models, but we will have comments about the formulation of the t ask, the features used to describe behavior, the source of data about user preferences, and similar is sues. This bias reflects our belief that strategies which have proved successful in other applicatio ns of machine learning will also serve us well in the design of adaptive interfaces. 3 Examples of Adaptive User Interfaces We can clarify the notion of an adaptive user interface by considering some e xamples that have appeared in the literature during recent years. Many of these systems focus on the generic task of information filtering, which involves directing a user’s attention toward items from a large s et that he is likely to find interesting or useful. Naturally, the most popul ar applications revolve around the World Wide Web, which provides both a wealth of information to fil ter and a convenient mechanism for interacting with users. However, the same basic techniques can be extended to broaderrecommendationtasks, such as suggesting products a consumer might want to buy. One example comes from Pazzani, Muramatsu, and Billsus (1996), who descri be SYSKILL & W EBERT, an adaptive interface which recommends web pages on a given topic that a user should find interesting. Much like typical search engines, this system pr sents the user with a list of web pages, but it also labels those candidates it predicts the user will esp ecially like or dislike. Moreover, it lets the user mark pages as desirable or undesirable, and the sys tem records the marked pages as training data for learning the user’s preferences. S YSKILL & W EBERT encodes each user model in terms of the probabilities that certain words will occur gi ven that the person likes (or dislikes) the document. The system invokes the naive Bayesian classifier to learn these probabilities and to predict whether the user will find a particular page des irable. This general approach to selection and learning is often referred to as c ntent-based filtering . Briefly, this scheme represents each item with a set of descriptors, usually t he words that occur in a document, and the filtering system uses these descriptors as predictive f ea ures when deciding whether to recommend a document to the user. This biases the selection process toward documents that are similar to ones the user has previously ranked highly. Ot er examples of adaptive user interfaces that embody the content-based approach include Lang’s (1995) NEWSWEEDER, which recommends news stories, and Boone’s (1998) Re:Agent, which sugg ests actions for handling electronic mail. Of course, content-based methods are also widely used in arch engines for the World Wide Web, and they predominate in the literature on inf ormation retrieval, but these typically do not employ learning algorithms to construct users mo dels. Another example of an adaptive interface is Shardanand and Maes’ (1995) R INGO, an interactive syste