Characterizing Search Behavior in Productivity Software

Complex software applications expose hundreds of commands to users through intricate menu hierarchies. One of the most popular productivity software suites, Microsoft Office, has recently developed functionality that allows users to issue free-form text queries to a search system to quickly find commands they want to execute, retrieve help documentation or access web results in a unified interface. In this paper, we analyze millions of search sessions originating from within Microsoft Office applications, collected over one month of activity, in an effort to characterize search behavior in productivity software. Our research brings together previous efforts in analyzing command usage in large-scale applications and efforts in understanding search behavior in environments other than the web. Our findings show that users engage primarily in command search, and that re-accessing commands through search is a frequent behavior. Our work represents the first large-scale analysis of search over command spaces and is an important first step in understanding how search systems integrated with productivity software can be successfully developed

Viewing Systems as Services: A Fresh Approach in the IS Field

Despite wide agreement that we are in a service-dominated economy, there has been little movement toward treating service and service metaphors as core aspects of the IS field. This tutorial proposes that viewing systems as services is a potentially fruitful but generally unexplored approach for thinking about systems in organizations, systems analysis, and numerous applications of IT. An extension of past research in several areas, viewing systems as services proves to be an umbrella for developing new systems analysis and design methods, improving business/IT communication, and finding practical paths toward greater relevance and significance in business and society

Personalization and usage data in academic libraries : an exploratory study

Personalization is a service pattern for ensuring proactive information delivery tailored to an individual based on learned or perceived needs of the person. It is credited as a remedy for information explosion especially in the academic environment and its importance to libraries was described to the extent of justifying their existence. There have been numerous novel approaches or technical specifications forwarded for realization of personalization in libraries. However, literature shows that the implementation of the services in libraries is minimal which implies the need for a thorough analysis and discussion of issues underlying the practicality of this service in the library environment. This study was initiated by this need and it was done with the objective of finding answers for questions related to library usage data, user profiles and privacy which are among the factors determining the success of personalized services in academic libraries. With the aim of finding comprehensive answers, five distinct cases representing different approaches to academic library personalization were chosen for thorough analysis and themes extracted from them was substantiated by extensive literature review. Moreover, with the aim of getting more information, unstructured questions were presented to the libraries running the services. The overall finding shows that personalization can be realized in academic libraries but it has to address issues related to collecting and processing user/usage data, user interest management, safeguarding user privacy, library privacy laws and other important matters discovered in the course of the study.Joint Master Degree in Digital Library Learning (DILL

Webometrics benefitting from web mining? An investigation of methods and applications of two research fields

Webometrics and web mining are two fields where research is focused on quantitative analyses of the web. This literature review outlines definitions of the fields, and then focuses on their methods and applications. It also discusses the potential of closer contact and collaboration between them. A key difference between the fields is that webometrics has focused on exploratory studies, whereas web mining has been dominated by studies focusing on development of methods and algorithms. Differences in type of data can also be seen, with webometrics more focused on analyses of the structure of the web and web mining more focused on web content and usage, even though both fields have been embracing the possibilities of user generated content. It is concluded that research problems where big data is needed can benefit from collaboration between webometricians, with their tradition of exploratory studies, and web miners, with their tradition of developing methods and algorithms

A Behavior-Driven Recommendation System for Stack Overflow Posts

Developers are often tasked with maintaining complex systems. Regardless of prior experience, there will inevitably be times in which they must interact with parts of the system with which they are unfamiliar. In such cases, recommendation systems may serve as a valuable tool to assist the developer in implementing a solution. Many recommendation systems in software engineering utilize the Stack Overflow knowledge-base as the basis of forming their recommendations. Traditionally, these systems have relied on the developer to explicitly invoke them, typically in the form of specifying a query. However, there may be cases in which the developer is in need of a recommendation but unaware that their need exists. A new class of recommendation systems deemed Behavior-Driven Recommendation Systems for Software Engineering seeks to address this issue by relying on developer behavior to determine when a recommendation is needed, and once such a determination is made, formulate a search query based on the software engineering task context. This thesis presents one such system, StackInTheFlow, a plug-in integrating into the IntelliJ family of Java IDEs. StackInTheFlow allows the user to intervi act with it as a traditional recommendation system, manually specifying queries and browsing returned Stack Overflow posts. However, it also provides facilities for detecting when the developer is in need of a recommendation, defined when the developer has encountered an error messages or a difficulty detection model based on indicators of developer progress is fired. Once such a determination has been made, a query formulation model constructed based on a periodic data dump of Stack Overflow posts will automatically form a query from the software engineering task context extracted from source code currently open within the IDE. StackInTheFlow also provides mechanisms to personalize, over time, the results displayed to a specific set of Stack Overflow tags based on the results previously selected by the user. The effectiveness of these mechanisms are examined and results based the collection of anonymous user logs and a small scale study are presented. Based on the results of these evaluations, it was found that some of the queries issued by the tool are effective, however there are limitations regarding the extraction of the appropriate context of the software engineering task yet to overcome

Learning from Unstructured Data to Monitor Human Health

The integration of mobile devices into our daily lives has created unique opportunities to improve human health and well-being. Many of these devices such as smartphones and smartwatches allow the users to enter unstructured data such as speech. This research is focused on utilizing such data for health monitoring through development of computational algorithms and optimization strategies that process unstructured data, compute health-related markers, and provide recommendations for improved health. The applications of this research include nutrition monitoring, dietary recommendation, personality assessment, and commonsense reasoning. Diet is known as an important lifestyle factor in self-management and prevention of chronic diseases. Although mobile and wearable sensors have been used to estimate eating context, accurate monitoring of dietary intake has remained a challenging problem. New approaches based on mobile devices have been proposed to facilitate the process of food intake recording. These technologies require individuals to use mobile devices such as smartphones to record nutrition intake by either entering text or taking images of the food. These technologies are prone to measurement errors related to challenges of human memory and bias. In order to address these limitations, we introduced development and validation of two nutrition monitoring frameworks, Speech2Health and EZNutriPal, which use unstructured data along with speech processing, natural language processing (NLP), and text mining techniques to facilitate dietary assessment.Implementing strategies that improve dietary intake is also very important. A general diet behavior change framework for joint nutrition monitoring and diet planning allows continuous diet recommendations for achieving a diet goal. This research introduces a diet planning framework, called iTell-uEat, to provide diet recommendations continuously based on user's diet habits. Two approaches are proposed including a reinforcement-learning-based and a greedy-based diet planning. An optimization algorithm is proposed to construct a meaningful action space for training reinforcement learning algorithms. Moreover, a linear optimization approach is developed forgreedy diet planning. To demonstrate the potential of utilizing unstructured data in applications beyond dietary assessment, a computational framework is proposed to analyze human personality traits based on expressed texts and to use these personalities for behavior and commonsense reasoning analysis

Using contextual and social links in information retrieval

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User Modeling and User Profiling: A Comprehensive Survey

The integration of artificial intelligence (AI) into daily life, particularly through information retrieval and recommender systems, has necessitated advanced user modeling and profiling techniques to deliver personalized experiences. These techniques aim to construct accurate user representations based on the rich amounts of data generated through interactions with these systems. This paper presents a comprehensive survey of the current state, evolution, and future directions of user modeling and profiling research. We provide a historical overview, tracing the development from early stereotype models to the latest deep learning techniques, and propose a novel taxonomy that encompasses all active topics in this research area, including recent trends. Our survey highlights the paradigm shifts towards more sophisticated user profiling methods, emphasizing implicit data collection, multi-behavior modeling, and the integration of graph data structures. We also address the critical need for privacy-preserving techniques and the push towards explainability and fairness in user modeling approaches. By examining the definitions of core terminology, we aim to clarify ambiguities and foster a clearer understanding of the field by proposing two novel encyclopedic definitions of the main terms. Furthermore, we explore the application of user modeling in various domains, such as fake news detection, cybersecurity, and personalized education. This survey serves as a comprehensive resource for researchers and practitioners, offering insights into the evolution of user modeling and profiling and guiding the development of more personalized, ethical, and effective AI systems.Comment: 71 page