Ubiquitous Recommendation Systems

I n many popular visions of ubiquitous computing, the environment proactively responds to individuals who inhabit the space. For example, a display magically presents a personalized advertisement, the most relevant video feed, or the desired page from a secret government document. Such capability requires more than an abundance of net-worked displays, devices, and sensors; it relies implicitly on recommendation systems that either directly serve the end user or provide critical services to some other application. In general, recommendation systems manage information overload by helping a user choose from among an overwhelming number of possibilities. These systems broadly fall into three classes based on the techniques they use to narrow the range of likely choices: • Content-based filtering systems utilize machine-learning techniques such as naïve Bayes to analyze Web pages, Usenet news, e-mail, and other types of electronic content amenable to automatic textual analysis. For example, such a system might compare words in an online film review with terms that characterize your movie-watching preferences to determine whether you are likely to enjoy that film. • Collaborative filtering systems ignore descriptions and instead focus on ratings of items by multiple users. For example, if a movie you have not seen is highly rated by others who share your taste in films, such a system might recommend that movie to you. • Link-based systems discover relations among items and then use graph-theoretic algorithms to find items in a set that are either the most exemplary or most referred to by other set members. Such approaches work well on hypertext and have been used to discover social networks. A link-based technique facilitates Google's good search results. Recommendation systems mediate the user experience in the digital world, and they will be increasingly helpful in performing the same role in the physical world, thereby filling an important gap in ubiquitous computing. By tailoring the environment or information they present, these systems refine the wealth of information, video feeds, online documents, and applications available through the pervasive infrastructure. Nevertheless, such systems face many difficult challenges before they can fulfill their supporting role in popular visions of ubiquitous computing. Constructing accurate user models and putting those models to proper use are among the most important challenges to developing next-generation recommendation systems. Filtering systems rely heavily on user models. Collaborative filtering systems cluster individual profiles to find highly rated items to recommend, which requires models to share the same …