Providing other people's trails for navigation assistance in physical environments

Asking other people the way has always been a widespread social wayfinding technique. In recent years wayfinding in unknown spatial environments has increasingly been supported by electronic navigation assistants. However, the social aspects of wayfinding, such as using other peoples’ experiences, have widely been ignored in the context of electronic navigation systems. In electronic environments such as the WWW the availability of community knowledge - also driven by Web 2.0 paradigms - becomes more and more valuable to users. Other people’s experiences, including recorded browsing paths and activities - so called user trails - are used for recommendation and navigation support and allow users to navigate vast information spaces more easily. In this paper we propose an approach for trail-based navigation in physical environments. An analysis and comparison of the concept of trails in both application areas establishes the basis for a trail model and the implementation of a trail-based navigation system prototype for mobile phones. Practical experiences with a prototype application and potential applications of trail-based navigation assistants are discussed in the context of a tourist scenario in the old town of Salzburg

The TrailTRECer Framework: Applying Open Hypermedia Concepts to Trails

Being lost in space and overloaded with information are two key problems users are confronted with, when searching for appropriate information. Trails built from information about the users' browsing paths and activities, are an established approach to assist users in navigating vast information spaces. However, existing trail-based systems are focusing on browsers only and therefore do not fully exploit the notion of trails. The TrailTRECer framework addresses these issues by being open to any application and any activity. The usability of the framework and the concept of user trails were tested by building a navigation support system with different trail-enabled clients

The TrailTRECer Framework: A platform for trail-enabled recommender applications

Abstract. In their everyday work people are confronted with ever growing amounts of information and thus often feel overloaded with data. Trails, built from information about the users' browsing paths and activities, are an established approach to assist users in navigating vast information spaces and finding appropriate information. While existing systems focus on web browsers only, we argue that trails can be generated by any application. We describe TrailTRECer, a framework which supports trail-based information access, and which is open to any application. The usability of the framework and the concept of user trails were tested by building a trail-enabled browser client and a print manager client. Initial user evaluations indicate the usefulness of this approach

Recommending Internet-Domains Using Trails and Neural Networks

Abstract. This paper discusses the use of artificial neural networks, trained with patterns extracted from trail data, as recommender systems. More specifically, feed-forward Multilayer-Perceptrons trained with the Backpropagation Algorithm were used to assign a rating to pairs of domains, based on the number of people that have traversed between them. This rating, applied to the hyper-graph neighborhood of an HTML document, can be used to suggest related domains to the user. The artificial neural network constructed in this project was capable of learning, and thus reproducing, the training set to a great extent. Outside of the training set, several experiments indicated that the artificial neural network becomes both capable of finding domains that are related, and an expert for domains that are relevant for the user community that produced the trail data