Showing papers on "User modeling published in 2011"

A Machine Learning Approach to Twitter User Classification

Abstract: This paper addresses the task of user classification in social media, with an application to Twitter. We automatically infer the values of user attributes such as political orientation or ethnicity by leveraging observable information such as the user behavior, network structure and the linguistic content of the user’s Twitter feed. We employ a machine learning approach which relies on a comprehensive set of features derived from such user information. We report encouraging experimental results on 3 tasks with different characteristics: political affiliation detection, ethnicity identification and detecting affinity for a particular business. Finally, our analysis shows that rich linguistic features prove consistently valuable across the 3 tasks and show great promise for additional user classification needs.

Tweets from Justin Bieber's heart: the dynamics of the location field in user profiles

Abstract: Little research exists on one of the most common, oldest, and most utilized forms of online social geographic information: the 'location' field found in most virtual community user profiles. We performed the first in-depth study of user behavior with regard to the location field in Twitter user profiles. We found that 34% of users did not provide real location information, frequently incorporating fake locations or sarcastic comments that can fool traditional geographic information tools. When users did input their location, they almost never specified it at a scale any more detailed than their city. In order to determine whether or not natural user behaviors have a real effect on the 'locatability' of users, we performed a simple machine learning experiment to determine whether we can identify a user's location by only looking at what that user tweets. We found that a user's country and state can in fact be determined easily with decent accuracy, indicating that users implicitly reveal location information, with or without realizing it. Implications for location-based services and privacy are discussed.

Analyzing user modeling on twitter for personalized news recommendations

Abstract: How can micro-blogging activities on Twitter be leveraged for user modeling and personalization? In this paper we investigate this question and introduce a framework for user modeling on Twitter which enriches the semantics of Twitter messages (tweets) and identifies topics and entities (e.g. persons, events, products) mentioned in tweets. We analyze how strategies for constructing hashtag-based, entity-based or topic-based user profiles benefit from semantic enrichment and explore the temporal dynamics of those profiles. We further measure and compare the performance of the user modeling strategies in context of a personalized news recommendation system. Our results reveal how semantic enrichment enhances the variety and quality of the generated user profiles. Further, we see how the different user modeling strategies impact personalization and discover that the consideration of temporal profile patterns can improve recommendation quality.

Automatically adapting user interfaces for hands-free interaction

Abstract: The method includes automatically, without user input and without regard to whether a digital assistant application has been separately invoked by a user, determining that the electronic device is in a vehicle. In some implementations, determining that the electronic device is in a vehicle comprises detecting that the electronic device is in communication with the vehicle (e.g., via a wired or wireless communication techniques and/or protocols). The method also includes, responsive to the determining, invoking a listening mode of a virtual assistant implemented by the electronic device. In some implementations, the method also includes limiting the ability of a user to view visual output presented by the electronic device, provide typed input to the electronic device, and the like.

System and method for implementing dynamic access control rules to personal cloud information

Abstract: A system and method are provided for sharing data of a user with contacts, the method comprising collecting the contacts from the user, collecting data related to the communications between the user and his contacts, and automatically grouping the contacts into different groups based on a level of communications between the user and the user's contacts, defining an access level for each group, with each access level granting access to some part of the user's data based on the access level. Advantageously, this permits a dynamic assignment of authority to access content that does not have to be actively managed by the user.

Semantic enrichment of twitter posts for user profile construction on the social web

Abstract: As the most popular microblogging platform, the vast amount of content on Twitter is constantly growing so that the retrieval of relevant information (streams) is becoming more and more difficult every day Representing the semantics of individual Twitter activities and modeling the interests of Twitter users would allow for personalization and therewith countervail the information overload Given the variety and recency of topics people discuss on Twitter, semantic user profiles generated from Twitter posts moreover promise to be beneficial for other applications on the Social Web as well However, automatically inferring the semantic meaning of Twitter posts is a non-trivial problem In this paper we investigate semantic user modeling based on Twitter posts We introduce and analyze methods for linking Twitter posts with related news articles in order to contextualize Twitter activities We then propose and compare strategies that exploit the semantics extracted from both tweets and related news articles to represent individual Twitter activities in a semantically meaningful way A large-scale evaluation validates the benefits of our approach and shows that our methods relate tweets to news articles with high precision and coverage, enrich the semantics of tweets clearly and have strong impact on the construction of semantic user profiles for the Social Web

User Modeling, Adaptation, and Personalization

Abstract: The social web is having a clear impact in our field of user modeling and adaptation. ‘Links’ and ‘Likes’ as well as ‘Followers’ and ‘Friends’ are part of a large source of data that is generated by users themselves, often for different purposes, and that provides an unprecedented potential for systems to understand their users and to adapt based on that understanding. As we can see from researchers and projects in a number of relevant fields, data on various manifestations of what users do socially on the web brings new opportunities. Exciting ideas are generated and first explorations show promising results. In this talk we take a look back at recent proposals and studies that consider the social web. We determine interesting patterns and we aim to understand the impact on methods and techniques for user modeling and adaptation. At the same time, the social web brings even more challenges. We look forward by identifying challenges that can drive our research. From technical challenges to explore the different social web sources to social challenges to understand how users behave when this potential is unlocked.

Personalizing web search using long term browsing history

Abstract: Personalizing web search results has long been recognized as an avenue to greatly improve the search experience. We present a personalization approach that builds a user interest profile using users' complete browsing behavior, then uses this model to rerank web results. We show that using a combination of content and previously visited websites provides effective personalization. We extend previous work by proposing a number of techniques for filtering previously viewed content that greatly improve the user model used for personalization. Our approaches are compared to previous work in offline experiments and are evaluated against unpersonalized web search in large scale online tests. Large improvements are found in both cases.

Disambiguation based on active input elicitation by intelligent automated assistant

Abstract: Methods, systems, and computer readable storage medium related to operating an intelligent digital assistant are disclosed. A user request is received, the user request including at least a speech input received from a user. Two or more alternative interpretations of user intent are obtained based on the received user request. One or more commonalities and one or more differences among the two or more alternative interpretations of user intent are identified. A response is provided to the user, the response presenting at least one of the identified differences and eliciting additional user input to choose among the two or more alternative interpretations of user intent based on the at least one difference.

Guided data repair

Abstract: In this paper we present GDR, a Guided Data Repair framework that incorporates user feedback in the cleaning process to enhance and accelerate existing automatic repair techniques while minimizing user involvement. GDR consults the user on the updates that are most likely to be beneficial in improving data quality. GDR also uses machine learning methods to identify and apply the correct updates directly to the database without the actual involvement of the user on these specific updates. To rank potential updates for consultation by the user, we first group these repairs and quantify the utility of each group using the decision-theory concept of value of information (VOI). We then apply active learning to order updates within a group based on their ability to improve the learned model. User feedback is used to repair the database and to adaptively refine the training set for the model. We empirically evaluate GDR on a real-world dataset and show significant improvement in data quality using our user guided repairing process. We also, assess the trade-off between the user efforts and the resulting data quality.

Audience-based presentation and customization of content

Abstract: A system and method are disclosed for delivering content customized to the specific user or users interacting with the system. The system includes one or more modules for recognizing an identity of a user. These modules may include for example a gesture recognition engine, a facial recognition engine, a body language recognition engine and a voice recognition engine. The user may also be carrying a mobile device such as a smart phone which identifies the user. One or more of these modules may cooperate to identify a user, and then customize the user's content based on the user's identity. In particular, the system receives user preferences indicating the content a user wishes to receive and the conditions under which it is to be received. Based on the user preferences and recognition of a user identity and/or other traits, the system presents content customized for a particular user.

Scalable distributed inference of dynamic user interests for behavioral targeting

Abstract: Historical user activity is key for building user profiles to predict the user behavior and affinities in many web applications such as targeting of online advertising, content personalization and social recommendations. User profiles are temporal, and changes in a user's activity patterns are particularly useful for improved prediction and recommendation. For instance, an increased interest in car-related web pages may well suggest that the user might be shopping for a new vehicle.In this paper we present a comprehensive statistical framework for user profiling based on topic models which is able to capture such effects in a fully \emph{unsupervised} fashion. Our method models topical interests of a user dynamically where both the user association with the topics and the topics themselves are allowed to vary over time, thus ensuring that the profiles remain current.We describe a streaming, distributed inference algorithm which is able to handle tens of millions of users. Our results show that our model contributes towards improved behavioral targeting of display advertising relative to baseline models that do not incorporate topical and/or temporal dependencies. As a side-effect our model yields human-understandable results which can be used in an intuitive fashion by advertisers.

Multicriteria User Modeling in Recommender Systems

Abstract: The paper mentions that a hybrid recommender systems framework creates user-profile groups before applying a collaborative-filtering algorithm by incorporating techniques from the multiple-criteria decision-analysis (MCDA) field.

System and method for dynamically allocating computing resources for processing security information

Abstract: Disclosed are systems, methods and computer program products for dynamically allocating computing resources for processing security information. In one example, the system receives from an antivirus application deployed on a user's computer information about user's actions related to the security of said computer. The system analyzes the received information to determine user's level of expertise in the field of computer security. The system then classifies the user into one of two or more different roles based on the determined level of expertise. The system automatically selects, based on the user's role, configuration setting of the antivirus application for collecting information about security threats detected by the user. The system also automatically allocates and configures, based on the user's role, computing resources and services for processing information collected by the antivirus application deployed on the user's computer about security threats detected by the user.

3d mobile user interface with configurable workspace management

Abstract: Systems and methods of a 3D mobile user interface with configurable workspace management are disclosed. In one aspect, embodiments of the present disclosure include a method, which may be implemented on a system, of a three-dimensional, multi-layer user interface of a mobile device in a mobile network. User environment may include one or more layers or levels of applications, services, or accounts that are all easily accessible to and navigable by the user. For example, an indicator can be used to access a workspace in 3D representing a category or grouping of services or applications for the user. The user can customize or create a unique, non-mutually exclusive grouping, aggregation, or category of applications, services, accounts, or items. The grouping of indicators can be used to swiftly and efficiently navigate to a desired application, service, account or item, in a 3D-enabled user environment.

Modeling and analysis of cross-session search tasks

Abstract: The information needs of search engine users vary in complexity, depending on the task they are trying to accomplish. Some simple needs can be satisfied with a single query, whereas others require a series of queries issued over a longer period of time. While search engines effectively satisfy many simple needs, searchers receive little support when their information needs span session boundaries. In this work, we propose methods for modeling and analyzing user search behavior that extends over multiple search sessions. We focus on two problems: (i) given a user query, identify all of the related queries from previous sessions that the same user has issued, and (ii) given a multi-query task for a user, predict whether the user will return to this task in the future. We model both problems within a classification framework that uses features of individual queries and long-term user search behavior at different granularity. Experimental evaluation of the proposed models for both tasks indicates that it is possible to effectively model and analyze cross-session search behavior. Our findings have implications for improving search for complex information needs and designing search engine features to support cross-session search tasks.

Recommender Systems: An Overview

Abstract: Recommender systems are tools for interacting with large and complex information spaces. They provide a personalized view of such spaces, prioritizing items likely to be of interest to the user. The field, christened in 1995, has grown enormously in the variety of problems addressed and techniques employed, as well as in its practical applications. Recommender systems research has incorporated a wide variety of artificial intelligence techniques including machine learning, data mining, user modeling, case-based reasoning, and constraint satisfaction, among others. Personalized recommendations are an important part of many on-line e-commerce applications such as Amazon.com, Netflix, and Pandora. This wealth of practical application experience has provided inspiration to researchers to extend the reach of recommender systems into new and challenging areas. The purpose of this special issue is to take stock of the current landscape of recommender systems research and identify directions the field is now taking. This article provides an overview of the current state of the field and introduces the various articles in the special issue.

User interface construction

Abstract: A system and method for constructing a graphical user interface for an application being accessed by a user are provided. The method includes measuring the user's current work rate, deriving a threshold from the user's current work rate, determining the user's current activity within the application, assigning a value to the user's current activity, accessing a model for the application, the model defining links between activities within the application, assigning values to activities linked within the accessed model to the user's current activity, and displaying a control element in a graphical user interface for each activity with a value above the derived threshold.

System and method for tracking web interactions with real time analytics

Abstract: A device, system and method is provided for monitoring a user's interactions with Internet-based programs or documents. Content may be extracted from Internet server traffic according to predefined rules. Extracted content may be associated with a user's Internet interaction. The user's Internet interaction may be stored and indexed. The user's Internet interaction may be analyzed to generate a recommendation provided to a contact center agent while the contact center agent is communicating with said user for guiding the user's interaction, for example, in real-time. Traffic other than Internet server traffic may also be used.

Modeling user search behavior for masquerade detection

Abstract: Masquerade attacks are a common security problem that is a consequence of identity theft. This paper extends prior work by modeling user search behavior to detect deviations indicating a masquerade attack. We hypothesize that each individual user knows their own file system well enough to search in a limited, targeted and unique fashion in order to find information germane to their current task. Masqueraders, on the other hand, will likely not know the file system and layout of another user's desktop, and would likely search more extensively and broadly in a manner that is different than the victim user being impersonated. We identify actions linked to search and information access activities, and use them to build user models. The experimental results show that modeling search behavior reliably detects all masqueraders with a very low false positive rate of 1.1%, far better than prior published results. The limited set of features used for search behavior modeling also results in large performance gains over the same modeling techniques that use larger sets of features.

Analyzing temporal dynamics in Twitter profiles for personalized recommendations in the social web

Abstract: Social Web describes a new culture of participation on the Web where more and more people actively participate in publishing and organizing Web content. As part of this culture, people leave a variety of traces when interacting with (other people via) Social Web systems. In this paper, we investigate user modeling strategies for inferring personal interest profiles from Social Web interactions. In particular, we analyze individual micro-blogging activities on Twitter. We compare different strategies for creating user profiles based on the Twitter messages a user has published and study how these profiles change over time. Moreover, we evaluate the quality of the user modeling strategies in the context of personalized recommender systems and show that those strategies which consider the temporal dynamics of the individual profiles allow for the best performance.

System effectiveness, user models, and user utility: a conceptual framework for investigation

Abstract: There is great interest in producing effectiveness measures that model user behavior in order to better model the utility of a system to its users. These measures are often formulated as a sum over the product of a discount function of ranks and a gain function mapping relevance assessments to numeric utility values. We develop a conceptual framework for analyzing such effectiveness measures based on classifying members of this broad family of measures into four distinct families, each of which reflects a different notion of system utility. Within this framework we can hypothesize about the properties that such a measure should have and test those hypotheses against user and system data. Along the way we present a collection of novel results about specific measures and relationships between them.

Network-based background expert

Abstract: A system and methodology that provides a network-based, e.g., cloud-based, background expert for predicting and/or accomplishing a user's goals is disclosed herein. Moreover, the system monitors, in the background, user generated data and/or publicly available data to determine and/or infer a user's goal, with or without an active indication/request from the user. Typically, the user-generated data can include user conversations, such as, but not limited to, speech data in a voice call, text messages, chat dialogs, etc. Further, the system identifies an action or task that facilitates accomplishment of the user goal in real-time. Moreover, the system can automatically perform the action/task and/or request user authorization prior to performing the action/task.

Continuous and non-intrusive identity verification in real-time environments based on free-text keystroke dynamics

Abstract: Internet services are important part of daily activities for most of us. These services come with sophisticated authentication requirements which may not be handled by average Internet users. The management of secure passwords for example creates an extra overhead which is often neglected due to usability reasons. Furthermore, password-based approaches are applicable only for initial logins and do not protect against unlocked workstation attacks. In this paper, we provide a non-intrusive identity verification scheme based on behavior biometrics where keystroke dynamics based-on free-text is used continuously for verifying the identity of a user in real-time. We improved existing keystroke dynamics based verification schemes in four aspects. First, we improve the scalability where we use a constant number of users instead of whole user space to verify the identity of target user. Second, we provide an adaptive user model which enables our solution to take the change of user behavior into consideration in verification decision. Next, we identify a new distance measure which enables us to verify identity of a user with shorter text. Fourth, we decrease the number of false results. Our solution is evaluated on a data set which we have collected from users while they were interacting with their mail-boxes during their daily activities.

Sample-efficient batch reinforcement learning for dialogue management optimization

Abstract: Spoken Dialogue Systems (SDS) are systems which have the ability to interact with human beings using natural language as the medium of interaction. A dialogue policy plays a crucial role in determining the functioning of the dialogue management module. Handcrafting the dialogue policy is not always an option, considering the complexity of the dialogue task and the stochastic behavior of users. In recent years approaches based on Reinforcement Learning (RL) for policy optimization in dialogue management have been proved to be an efficient approach for dialogue policy optimization. Yet most of the conventional RL algorithms are data intensive and demand techniques such as user simulation. Doing so, additional modeling errors are likely to occur. This paper explores the possibility of using a set of approximate dynamic programming algorithms for policy optimization in SDS. Moreover, these algorithms are combined to a method for learning a sparse representation of the value function. Experimental results show that these algorithms when applied to dialogue management optimization are particularly sample efficient, since they learn from few hundreds of dialogue examples. These algorithms learn in an off-policy manner, meaning that they can learn optimal policies with dialogue examples generated with a quite simple strategy. Thus they can learn good dialogue policies directly from data, avoiding user modeling errors.

User model interoperability: a survey

Abstract: Nowadays a large number of user-adaptive systems has been developed. Commonly, the effort to build user models is repeated across applications and domains, due to the lack of interoperability and synchronization among user-adaptive systems. There is a strong need for the next generation of user models to be interoperable, i.e. to be able to exchange user model portions and to use the information that has been exchanged to enrich the user experience. This paper presents an overview of the well-established literature dealing with user model interoperability, discussing the most representative work which has provided valuable solutions to face interoperability issues. Based on a detailed decomposition and a deep analysis of the selected work, we have isolated a set of dimensions characterizing the user model interoperability process along which the work has been classified. Starting from this analysis, the paper presents some open issues and possible future deployments in the area.

Past, Present, and Future of Model-Based User Interface Development

Abstract: This article presents the past, present and future of model-based user interface development. After 30 years of research there has been significant success in modeling user interfaces. This article aims to give a comprehensive overview of the history, describes important aspects and current approaches, lists actual challenges of model-based user interface development and gives implications for the next generation.

User tracking feedback

Abstract: Technology is presented for providing feedback to a user on an ability of an executing application to track user action for control of the executing application on a computer system. A capture system detects a user in a capture area. Factors in the capture area and the user's actions can adversely affect the ability of the application to determine if a user movement is a gesture which is a control or instruction to the application. One example of such factors is a user being out of the field of view of the capture system. Some other factor examples include lighting conditions and obstructions in the capture area. Responsive to a user tracking criteria not being satisfied, feedback is output to the user. In some embodiments, the feedback is provided within the context of an executing application.

User-click modeling for understanding and predicting search-behavior

Abstract: Recent advances in search users' click modeling consider both users' search queries and click/skip behavior on documents to infer the user's perceived relevance. Most of these models, including dynamic Bayesian networks (DBN) and user browsing models (UBM), use probabilistic models to understand user click behavior based on individual queries. The user behavior is more complex when her actions to satisfy her information needs form a search session, which may include multiple queries and subsequent click behaviors on various items on search result pages. Previous research is limited to treating each query within a search session in isolation, without paying attention to their dynamic interactions with other queries in a search session.Investigating this problem, we consider the sequence of queries and their clicks in a search session as a task and propose a task-centric click model~(TCM). TCM characterizes user behavior related to a task as a collective whole. Specifically, we identify and consider two new biases in TCM as the basis for user modeling. The first indicates that users tend to express their information needs incrementally in a task, and thus perform more clicks as their needs become clearer. The other illustrates that users tend to click fresh documents that are not included in the results of previous queries. Using these biases, TCM is more accurately able to capture user search behavior. Extensive experimental results demonstrate that by considering all the task information collectively, TCM can better interpret user click behavior and achieve significant improvements in terms of ranking metrics of NDCG and perplexity.