Understanding Of User Intentions Research Articles

Search Query Refinement Using Context, Knowledge and Long-term Memorization

In the era of information overload, search tools are crucial, yet users often struggle to articulate their precise dataneeds, resulting in sub-optimal search outcomes. This is often due to users associating products with influencers or celebrities rather than knowing the specific brand or product name. This research aims to enable users to find products based on real-life associ- ations, emphasizing the importance of upgrading search query refinement for accuracy and relevance. A significant challenge faced by existing web tools is refining queries involving unrelated entities. This research addresses this gap by proposing a compre- hensive approach that integrates context, knowledge represen- tation, and long-term memorization. The framework combines contextual information with advanced knowledge representation techniques, enhancing the system’s understanding of user intent and domain-specific concepts. Long-term memorization reduces the time complexity of query refinement by leveraging past search experiences. This study underscores the potential of incorporating context, knowledge representation, and long-term memorization in refining search queries, offering more accurate results. As the digital landscape evolves, our approach has the potential to upgrade the search engine experience, providing personalized and contextually relevant results globally.

Understanding the diffusion and adoption of Bitcoin transaction services: The integrated approach

This study proposes a comprehensive user acceptance model for Bitcoin transaction services, while also statistically presenting how four user-oriented theories (innovation diffusion theory, the benefit-risk concept, the theory of planned behavior, and transaction cost theory) contribute to the understanding of user intent regarding the use of these services. Results from more than 1300 samples suggest that both perceived benefits and service compatibility play notable roles in determining behavioral intent, while perceived risk, cost, and complexity were found to have no significant impact on user adoption. Moreover, both the potential trialability and observability of the services were investigated as weak antecedents of user adoption. On the basis of these results, the resulting managerial and theoretical implications are examined, albeit with notable limitations.

HILC

Creating automation scripts for tasks involving Graphical User Interface (GUI) interactions is hard. It is challenging because not all software applications allow access to a program’s internal state, nor do they all have accessibility APIs. Although much of the internal state is exposed to the user through the GUI, it is hard to programmatically operate the GUI’s widgets. To that end, we developed a system prototype that learns by demonstration, called HILC (Help, It Looks Confusing). Users, both programmers and non-programmers, train HILC to synthesize a task script by demonstrating the task. A demonstration produces the needed screenshots and their corresponding mouse-keyboard signals. After the demonstration, the user answers follow-up questions. We propose a user-in-the-loop framework that learns to generate scripts of actions performed on visible elements of graphical applications. Although pure programming by demonstration is still unrealistic due to a computer’s limited understanding of user intentions, we use quantitative and qualitative experiments to show that non-programming users are willing and effective at answering follow-up queries posed by our system, to help with confusing parts of the demonstrations. Our models of events and appearances are surprisingly simple but are combined effectively to cope with varying amounts of supervision. The best available baseline, Sikuli Slides, struggled to assist users in the majority of the tests in our user study experiments. The prototype with our proposed approach successfully helped users accomplish simple linear tasks, complicated tasks (monitoring, looping, and mixed), and tasks that span across multiple applications. Even when both systems could ultimately perform a task, ours was trained and refined by the user in less time.

A flexible graph-based model for facilitating digital learning activities

The development of big data techniques has created great opportunities for more powerful e-learning systems. Online platforms such as MOOCs, distance learning communities and mobile learning applications are therefore able to have larger data capacity and allow more users to access. However, a big challenge accompanied with the rapid development of e-learning systems is information overload for learners. Users may get lost in such a large volume of learning resources. To address this issue, it is essential to have a model for better understanding of user intentions, preferences and prior knowledge. Therefore, in this article, a flexible graph-based model for users is proposed by incorporating various relationships in this context. By consolidating various kinds of relationships in a unified graph model for the users, the model can then facilitate digital learning activities in e-learning systems. Additionally, some examples of using this model are discussed for the future studies on the model.