No abstract

No abstract

In this paper we introduce a novel approach for generating an intention prediction model of user interactions with systems. As part of this new approach, we include personal aspects such as user characteristics that can increase prediction accuracy. The model is automatically trained according to the user's fixed attributes (e.g., demographic data such as age and gender) and the user's sequences of actions in the system. The generated model has a tree structure. The building blocks of each node can be any probabilistic sequence model (such as hidden Markov models and conditional random fields)) and each node is split according to user attributes. Thus, we refer to this algorithm as an attribute-driven model tree. The new model was first tested on simulated data in which users with different attributes (such as age, gender) behave differently when trying to accomplish various tasks. We then validated the ability of the algorithm to discover the relevant attributes. We tested our algorithm on two real datasets: from a Web application and a mobile application dataset. The results were encouraging and indicate the capability of the proposed method for discovering the correct user intention model and increasing intention prediction accuracy compared to single HMM or CRF models. The motivation of this paper is to achieve a higher level of compatibility between user intentions and the system she is using. This motivation derives from the fact that in recent years applications have continuously been gaining functionality and emerging features are complicating user interactions with applications. Using intention prediction methods will enable a company to improve the services provided to users by adjusting the system services to their needs (e.g., the interface). Predicting user intentions can be also used for assisting users to perform or complete tasks, to alert them regarding the availability of features, and to increase their general knowledge. Effective assistance will allow users to acquire the new skills and knowledge they need to easily operate an unfamiliar device or function. Different users tend to select different sequences for accomplishing the same goal. Specifically, our hypothesis is that the user's attributes and context (such as age or the operating system) indicate which sequence the user will eventually perform. This hypothesis is based on studies that examined the interaction of users with systems (such as Web browsers) and found that user attributes affect the way of interaction. [For example, Hu et al., 2007, Weber and Castillo 2010; Thakor et al., 2004]. If a young male uses a system, it is possible to use the model to predict the goal that he intends to accomplish and provide him with a user interface (UI) more appropriate to his usage and intentions. Although methods exist for predicting user intentions, they do not take into account user attributes that can increase the accuracy of prediction. In this work we present a new method that we developed that examines how employing user attributes can...

Providing adaptive help during interaction with the system can be used to assist users in accomplishing their tasks. We propose providing guidance by highlighting the steps required for performing a task that the user intends to complete according to the prediction of a system. We present a study aimed at examining whether highlighting intended user steps in menus and toolbars as a means of assisting users in performing tasks is useful in terms of user response and performance. We also examined the effects of different accuracy levels of the relevancy of the provided help and the help format on user response and performance. An experiment was conducted in which 64 participants performed tasks using menus and toolbars of a simulated email application. Participants were offered a highlighted guidance of the required steps in varying levels of accuracy (100%, 80%, 60%, and no guidance). Our results support the benefits of highlighted help both in user performance times and in user satisfaction from receiving such assistance. Users found the assistance necessary and helpful and by the same token not unduly intrusive. Additionally, users felt that such assistance generally helped in reducing performance time on tasks. We did not find a significant difference when users receiving help at 80% accuracy was compared to those receiving help at 100% accuracy; however, such a difference does appear for 2 those receiving 60% accuracy. In such cases we found that the user's satisfaction level, perceived usefulness and trust in the system decreased while their notion of perceived intrusiveness increased. We conclude that assisting users by highlighting the required steps is useful so long as the minimal accuracy level is higher than 60%.Our study has implications on the implementation of highlighting next steps as a means of adaptive help and on integrating probability-based algorithms such as intention prediction to adaptive assistance systems.

No abstract