Showing papers on "User modeling published in 2014"

Communications network interface for user friendly interactive access to online services

Abstract: A method and a system for connecting a service provider and a user at a remote location relative to the service provider, via a network based telecommunications device, are provided. The method includes utilizing a network operable terminal for transmitting communications between the service provider and the user, employing a display screen depicting a user selectable options menu, corresponding with service functions offered by the service provider, and enabling the user to choose an option from the user selectable options menu to initiate a corresponding communication to the service provider. The system includes a user operable terminal including a user interface display screen with user selectable menu options that are changeable in accordance with differing modes of operation, an internal processing unit configured for providing at least one selectable menu option; and a gateway service platform configured for transmitting an option selected from the at least one selectable menu option to and from one of the user or the provider.

Presentation and analysis of user interaction data

Abstract: An interactive, graph-based user interaction data analysis system is disclosed. The system is configured to provide analysis and graphical visualizations of user interaction data to a system operator. In various embodiments, interactive visualizations and analyses provided by the system may be based on user interaction data aggregated across particular groups of users, across particular time frames, and/or from particular computer-based platforms and/or applications. According to various embodiments, the system may enable insights into, for example, user interaction patterns and/or ways to optimize for desired user interactions, among others. In an embodiment, the system allows an operator to analyze and investigate user interactions with content provided via one or more computer-based platforms, software applications, and/or software application editions.

Measuring User Engagement

Abstract: User engagement refers to the quality of the user experience that emphasizes the positive aspects of interacting with an online application and, in particular, the desire to use that application longer and repeatedly. User engagement is a key concept in the design of online applications (whether for desktop, tablet or mobile), motivated by the observation that successful applications are not just used, but are engaged with. Users invest time, attention, and emotion in their use of technology, and seek to satisfy pragmatic and hedonic needs. Measurement is critical for evaluating whether online applications are able to successfully engage users, and may inform the design of and use of applications. User engagement is a multifaceted, complex phenomenon; this gives rise to a number of potential measurement approaches. Common ways to evaluate user engagement include using self-report measures, e.g., questionnaires; observational methods, e.g. facial expression analysis, speech analysis; neuro-physiological signal processing methods, e.g., respiratory and cardiovascular accelerations and decelerations, muscle spasms; and web analytics, e.g., number of site visits, click depth. These methods represent various trade-offs in terms of the setting (laboratory versus ``in the wild''), object of measurement (user behaviour, affect or cognition) and scale of data collected. For instance, small-scale user studies are deep and rich, but limited in terms of generalizability, whereas large-scale web analytic studies are powerful but negate users' motivation and context. The focus of this book is how user engagement is currently being measured and various considerations for its measurement. Our goal is to leave readers with an appreciation of the various ways in which to measure user engagement, and their associated strengths and weaknesses. We emphasize the multifaceted nature of user engagement and the unique contextual constraints that come to bear upon attempts to measure engagement in different settings, and across different user groups and web domains. At the same time, this book advocates for the development of ``good'' measures and good measurement practices that will advance the study of user engagement and improve our understanding of this construct, which has become so vital in our wired world. Table of Contents: Preface / Acknowledgments / Introduction and Scope / Approaches Based on Self-Report Methods / Approaches Based on Physiological Measurements / Approaches Based on Web Analytics / Beyond Desktop, Single Site, and Single Task / Enhancing the Rigor of User Engagement Methods and Measures / Conclusions and Future Research Directions / Bibliography / Authors' Biographies / Index

Interactive media guidance application with intelligent navigation and display features

Abstract: Methods and systems are disclosed that allow a user to efficiently navigate media selections in an interactive media guidance application and easily identify media for viewing. The disclosed methods and systems provide an environment wherein video assets are displayed according to a user preference on a mosaic page with multiple cells. A subset of the assets appropriate for display in a particular cell is determined based on the user preference. Relevance scores of the assets meeting the user preference are computed, and the asset having the greatest relevance for the user is selected and displayed the corresponding cell. The relevance scores can be computed based on the user's historic viewing habits, user interaction with a media guidance application, or on specific user input.

Interactive user interface

Abstract: Disclosed is an interactive, customizable, user interaction data analysis system that provides interactive, customizable, compact and information dense user interaction data analysis via a graphical user interface. This can cohort-based analysis and/or graphical visualizations of user interaction data to a system operator. User interaction data may be obtained, for example, as users interact with one or more software applications. In various embodiments, interactive and customizable visualizations and analyses provided by the system may be based on user interaction data aggregated across groups of users (also referred to as cohorts of users), across particular time frames, and/or from particular software and/or computer-based applications. According to various embodiments, the system may enable insights into, for example, user interaction patterns, the frequency of software application features accessed, the performance of various aspects of software applications, and/or crashes of software applications, among others.

Better resolution when referencing to concepts

Abstract: Systems and processes for operating a virtual assistant programmed to refer to shared domain concepts using concept nodes are provided. In some examples, to process a textual representation of user speech using an active ontology having these concept nodes, a primary user intent can be determined from the textual representation of user speech. Concepts referred to by the primary user intent can be identified, and substrings of the textual representation of user speech corresponding to the concepts can be identified. Secondary user intents for the substrings can be determined and a task flow based on the primary user intent and the secondary user intents can be generated and performed.

Modeling User Preferences in Recommender Systems: A Classification Framework for Explicit and Implicit User Feedback

Abstract: Recommender systems are firmly established as a standard technology for assisting users with their choices; however, little attention has been paid to the application of the user model in recommender systems, particularly the variability and noise that are an intrinsic part of human behavior and activity. To enable recommender systems to suggest items that are useful to a particular user, it can be essential to understand the user and his or her interactions with the system. These interactions typically manifest themselves as explicit and implicit user feedback that provides the key indicators for modeling users’ preferences for items and essential information for personalizing recommendations. In this article, we propose a classification framework for the use of explicit and implicit user feedback in recommender systems based on a set of distinct properties that include Cognitive Effort, User Model, Scale of Measurement, and Domain Relevance. We develop a set of comparison criteria for explicit and implicit user feedback to emphasize the key properties. Using our framework, we provide a classification of recommender systems that have addressed questions about user feedback, and we review state-of-the-art techniques to improve such user feedback and thereby improve the performance of the recommender system. Finally, we formulate challenges for future research on improvement of user feedback.

Computer-implemented method and system for enabling communication between networked users based on common characteristics

Abstract: A computer-implemented method and system for enabling communication between networked users based on search queries and common characteristics is disclosed. Particular embodiments relate to receiving a search query from a first user and establishing a communication link between the first user and a second user based on the first user's search query. Particular embodiments relate to receiving a first search query from a first user, receiving a second search query from a second user, determining if the first user and the second user fit within match criteria, and establishing a communication link between the first user and the second user if the first user and the second user fit within match criteria. Particular embodiments relate to receiving a first search query from a first user, receiving a second search query from a second user, determining if the first search query and the second search query fit within match criteria, determining if the first user and the second user fit within match criteria, and establishing a communication link between the first user and the second user if the first search query and the second search query fit within match criteria and if the first user and the second user fit within match criteria.

The SWELL Knowledge Work Dataset for Stress and User Modeling Research

Abstract: This paper describes the new multimodal SWELL knowledge work (SWELL-KW) dataset for research on stress and user modeling. The dataset was collected in an experiment, in which 25 people performed typical knowledge work (writing reports, making presentations, reading e-mail, searching for information). We manipulated their working conditions with the stressors: email interruptions and time pressure. A varied set of data was recorded: computer logging, facial expression from camera recordings, body postures from a Kinect 3D sensor and heart rate (variability) and skin conductance from body sensors. The dataset made available not only contains raw data, but also preprocessed data and extracted features. The participants' subjective experience on task load, mental effort, emotion and perceived stress was assessed with validated questionnaires as a ground truth. The resulting dataset on working behavior and affect is a valuable contribution to several research fields, such as work psychology, user modeling and context aware systems.

Method and system utilizing a personalized user model to develop a search request

Abstract: A method and system for developing a computer-executable query relating to a search request issued by a user includes determining whether a search term of the search request has a user-specific meaning, connotation, context, or association based on an analysis of electronic content associated with the user and/or an analysis of interactions of the user with electronic content that is accessible to the user through one or more computing devices. If the search term has a user-specific meaning, connotation, context, or association, the method and system can incorporate the user-specific meaning, connotation, context, or association into the search request and/or the computer-executable query.

Plan, Activity, and Intent Recognition: Theory and Practice

Abstract: Plan recognition, activity recognition, and intent recognition together combine and unify techniques from user modeling, machine vision, intelligent user interfaces, human/computer interaction, autonomous and multi-agent systems, natural language understanding, and machine learning. Plan, Activity, and Intent Recognition explains the crucial role of these techniques in a wide variety of applications including: personal agent assistants, computer and network security, opponent modeling in games and simulation systems, coordination in robots and software agents, web e-commerce and collaborative filtering, dialog modeling, video surveillance, smart homes In this book, follow the history of this research area and witness exciting new developments in the field made possible by improved sensors, increased computational power, and new application areas. Combines basic theory on algorithms for plan/activity recognition along with results from recent workshops and seminars Explains how to interpret and recognize plans and activities from sensor data Provides valuable background knowledge and assembles key concepts into one guide for researchers or students studying these disciplines

Overview user interface of emergency call data of a law enforcement agency

Abstract: Techniques in this disclosure may provide a user interface that concurrently displays multiple panels which provide visualization of emergency call data of a law enforcement agency. The user interface can provide a high-level overview of emergency calls in a geographical area. Each panel in the user interface can provide visualization of the emergency calls and/or statistics relating to the calls. A user can customize which panels to include in the user interface and/or customize setting for each panel. The user may apply various types of filters to the data displayed in the user interface, and the panels can update the visualizations according to the filters. The user interface can also provide the ability to show data at various levels of detail within the same user interface or panel. The techniques in the disclosure can provide a convenient, digestible overview of tactical and/or strategic data in a single user interface.

User training by intelligent digital assistant

Abstract: The method includes receiving, from a user, a first speech input spoken in a first language; inferring a user intent based on at least the first speech input in the first language; based on the inferred user intent, generating one or more alternative expressions of the first speech input in the first language; and providing feedback to the user introducing the alternative expressions as a more preferred input to express the inferred user intent than the first speech input provided by the user.

Choice-based preference elicitation for collaborative filtering recommender systems

Abstract: We present an approach to interactive recommending that combines the advantages of algorithmic techniques with the benefits of user-controlled, interactive exploration in a novel manner. The method extracts latent factors from a matrix of user rating data as commonly used in Collaborative Filtering, and generates dialogs in which the user iteratively chooses between two sets of sample items. Samples are chosen by the system for low and high values of each latent factor considered. The method positions the user in the latent factor space with few interaction steps, and finally selects items near the user position as recommendations. In a user study, we compare the system with three alternative approaches including manual search and automatic recommending. The results show significant advantages of our approach over the three competing alternatives in 15 out of 24 possible parameter comparisons, in particular with respect to item fit, interaction effort and user control. The findings corroborate our assumption that the proposed method achieves a good trade-off between automated and interactive functions in recommender systems.

Method and Apparatus for Building a User Profile, for Personalization Using Interaction Data, and for Generating, Identifying, and Capturing User Data Across Interactions Using Unique User Identification

Abstract: A user profile is creates, and personalization is provided, by compiling interaction data. The interaction data is compiled to generate a value index or score from a user model. Parameterized data is used to build tools which help decide an engagement strategy and modes of engagement with a user. Several facets relating to the user, such as user behavior, user interests, products bought, intent, chat language, and so on, are compiled to create a user profile based personalization technique. In another embodiment, a unique ID is provided that can be mapped across multiple channels for use by the user to contact various organizations across multiple channels, and thus upgrade the user's experience.

Human-building interaction framework for personalized comfort driven system operations in buildings

Abstract: A computer data processing system may provide control information for controlling how an environmental control system controls an environment within a building. The computer data processing system may receive and store reports from multiple users and/or may receive and store reports at different times from a user. Each report may provide information concerning how the user perceives the comfort level of the user's environment at the time the user supplies the information. The computer data processing system may determine and generate the control information for controlling how the environmental control system controls the environment based on the information concerning how each user perceives the comfort level of the user's environment at the time each user provides the information. In addition or instead, the computer data processing system may determine and generate such control information based on the information concerning how a user perceives the comfort level of the user's environment at the different times the user supplies the information.

Analyzing user behavior across social sharing environments

Abstract: In this work we present an in-depth analysis of the user behaviors on different Social Sharing systems. We consider three popular platforms, Flickr, Delicious and StumbleUpon, and, by combining techniques from social network analysis with techniques from semantic analysis, we characterize the tagging behavior as well as the tendency to create friendship relationships of the users of these platforms. The aim of our investigation is to see if (and how) the features and goals of a given Social Sharing system reflect on the behavior of its users and, moreover, if there exists a correlation between the social and tagging behavior of the users. We report our findings in terms of the characteristics of user profiles according to three different dimensions: (i) intensity of user activities, (ii) tag-based characteristics of user profiles, and (iii) semantic characteristics of user profiles.

Collaborative personalized Twitter search with topic-language models

Abstract: The vast amount of real-time and social content in microblogs results in an information overload for users when searching microblog data. Given the user's search query, delivering content that is relevant to her interests is a challenging problem. Traditional methods for personalized Web search are insufficient in the microblog domain, because of the diversity of topics, sparseness of user data and the highly social nature. In particular, social interactions between users need to be considered, in order to accurately model user's interests, alleviate data sparseness and tackle the cold-start problem. In this paper, we therefore propose a novel framework for Collaborative Personalized Twitter Search. At its core, we develop a collaborative user model, which exploits the user's social connections in order to obtain a comprehensive account of her preferences. We then propose a novel user model structure to manage the topical diversity in Twitter and to enable semantic-aware query disambiguation. Our framework integrates a variety of information about the user's preferences in a principled manner. A thorough evaluation is conducted using two personalized Twitter search query logs, demonstrating a superior ranking performance of our framework compared with state-of-the-art baselines.

Design, User Experience, and Usability. User Experience Design for Diverse Interaction Platforms and Environments

Abstract: We solve a standing issue of the recently published Common Implementation Framework (CIF) for Online Virtual Museums: programmatic access to the transcoding, optimization and template rendering infrastructure of the CIF. We propose a method that enables researchers and developers to build novel systems on top of the CIF infrastructure beyond its current Cultural Heritage workflow. Therefore, we introduce a way to programmatically access the powerful backend of the CIF through a universal access layer, addressable by standards like HTTP and the JSON Data Interchange Format. In order to demonstrate our approach, we present two different use cases in which the CIF pipeline is utilized as a service through the proposed resource-based access layer: a native mobile iOS application for browsing 3D model repositories realizing just-in-time optimization of large models, and a MeshLab plugin to asynchronously convert and prepare a model for the Web.

Context adaptation in interactive recommender systems

Abstract: Contextual factors can greatly influence the utility of recommendations for users. In many recommendation and personalization applications, particularly in domains where user context changes dynamically, it is difficult to represent and model contextual factors directly, but it is often possible to observe their impact on user preferences during the course of users' interactions with the system. In this paper, we introduce an interactive recommender system that can detect and adapt to changes in context based on the user's ongoing behavior. The system, then, dynamically tailors its recommendations to match the user's most recent preferences. We formulate this problem as a multi-armed bandit problem and use Thompson sampling heuristic to learn a model for the user. Following the Thompson sampling approach, the user model is updated after each interaction as the system observes the corresponding rewards for the recommendations provided during that interaction. To generate contextual recommendations, the user's preference model is monitored for changes at each step of interaction with the user and is updated incrementally. We will introduce a mechanism for detecting significant changes in the user's preferences and will describe how it can be used to improve the performance of the recommender system.

Adaptive User Interfaces

Abstract: According to various embodiments of the present invention, user performance and/or motivation for a computing system may be maximized by optimizing one or more target components of a user interface of the computing system. The target components may be aspects of the user interface that is perceived by the user. One or more input features and one or more output features may be identified, and data regarding these input and output features may be gathered. This data may be compared with the results generated by a set of candidate artificial intelligence algorithms to determine which of them provides the best fit with the data collected. Then, the selected artificial intelligence algorithm may be applied to the user interface to iteratively change the target components over time until the optimal settings for each user are discovered.

Systems and methods for authenticating a user based on a biometric model associated with the user

Abstract: Systems and methods as provided herein may create a biometric model associated with a user. The created biometric model may be used to generate challenges that are presented to the user for authentication purposes. A user response to the challenge may be compared to an expected response, and if the user response matches within a predetermined error of the expected response, the user may be authenticated. The systems and methods may further generate challenges that are adaptively designed to address weaknesses or errors in the created model such that the model is more closely associated with a user and the user is more likely to be the only person capable of successfully responding to the generated challenges.

A method and system of providing data service according to a user's future location

Abstract: According to one aspect of the present invention there is provided a method for controlling data service for a vehicle-based user of a telecommunication network. The method comprising: retrieving data associated with location behaviour of the user within the network; predicting, based on said data and on a current user location within the network, a future user location within the network; performing a determination of network capability at the future user location; and modifying, in response to the determination, a configuration of the network and/or a service parameter associated with the user. A network element, system and computer program product are also provided.

A Survey on Supervised Classification on Data Streams

Abstract: The last ten years were prolific in the statistical learning and data mining field and it is now easy to find learning algorithms which are fast and automatic. Historically a strong hypothesis was that all examples were available or can be loaded into memory so that learning algorithms can use them straight away. But recently new use cases generating lots of data came up as for example: monitoring of telecommunication network, user modeling in dynamic social network, web mining, etc. The volume of data increases rapidly and it is now necessary to use incremental learning algorithms on data streams. This article presents the main approaches of incremental supervised classification available in the literature. It aims to give basic knowledge to a reader novice in this subject.