Statistical topic models have become a useful and ubiquitous tool for analyzing large text corpora. One common application of statistical topic models is to support topic-centric navigation and exploration of document collections. Existing work on topic modeling focuses on the inference of model parameters so the resulting model fits the input data. Since the exact inference is intractable, statistical inference methods, such as Gibbs Sampling, are commonly used to solve the problem. However, most of the existing work ignores an important aspect that is closely related to the end user experience: topic model stability. When the model is either re-trained with the same input data or updated with new documents, the topic previously assigned to a document may change under the new model, which may result in a disruption of end users’ mental maps about the relations between documents and topics, thus undermining the usability of the applications. In this article, we propose a novel user-directed non-disruptive topic model update method that balances the tradeoff between finding the model that fits the data and maintaining the stability of the model from end users’ perspective. It employs a novel constrained LDA algorithm to incorporate pairwise document constraints, which are converted from user feedback about topics, to achieve topic model stability. Evaluation results demonstrate the advantages of our approach over previous methods.

Users with anomalous behaviors in online communication systems (e.g. email and social medial platforms) are potential threats to society. Automated anomaly detection based on advanced machine learning techniques has been developed to combat this issue; challenges remain, though, due to the difficulty of obtaining proper ground truth for model training and evaluation. Therefore, substantial human judgment on the automated analysis results is often required to better adjust the performance of anomaly detection. Unfortunately, techniques that allow users to understand the analysis results more efficiently, to make a confident judgment about anomalies, and to explore data in their context, are still lacking. In this paper, we propose a novel visual analysis system, TargetVue, which detects anomalous users via an unsupervised learning model and visualizes the behaviors of suspicious users in behavior-rich context through novel visualization designs and multiple coordinated contextual views. Particularly, TargetVue incorporates three new ego-centric glyphs to visually summarize a user's behaviors which effectively present the user's communication activities, features, and social interactions. An efficient layout method is proposed to place these glyphs on a triangle grid, which captures similarities among users and facilitates comparisons of behaviors of different users. We demonstrate the power of TargetVue through its application in a social bot detection challenge using Twitter data, a case study based on email records, and an interview with expert users. Our evaluation shows that TargetVue is beneficial to the detection of users with anomalous communication behaviors.

Statistical topic models have become a useful and ubiquitous text analysis tool for large corpora. One common application of statistical topic models is to support topic-centric navigation and exploration of document collections at the user interface by automatically grouping documents into coherent topics. For today's constantly expanding document collections, topic models need to be updated when new documents become available. Existing work on topic model update focuses on how to best fit the model to the data, and ignores an important aspect that is closely related to the end user experience: topic model stability. When the model is updated with new documents, the topics previously assigned to old documents may change, which may result in a disruption of end users' mental maps between documents and topics, thus undermining the usability of the applications. In this paper, we describe a user-directed non-disruptive topic model update system, nTMU, that balances the tradeoff between finding the model that fits the data and maintaining the stability of the model from end users' perspective. It employs a novel constrained LDA algorithm (cLDA) to incorporate pair-wise document constraints, which are converted from user feedback about topics, to achieve topic model stability. Evaluation results demonstrate advantages of our approach over previous methods.

In this paper, we present Expediting Expertise, a system designed to provide structured support to the otherwise informal process of social learning in the enterprise. It employs a data-driven approach where online content is automatically analyzed and categorized into relevant topics, topic-specific user expertise is calculated by comparing the models of individual users against those of the experts, and personalized recommendation of learning activities is created accordingly to facilitate expertise development. The system's UI is designed to provide users with ongoing feedback of current expertise, progress, and comparison with others. Learning recommendation is visualized with an interactive treemap which presents estimated return on investment and distance to current expertise for each recommended learning activity. Evaluation of the system showed very positive results.

We present Enterprise Priority Inbox Classifier (EPIC), an automatic personalized email prioritization system based on a topic-based user model built from the user's email data and relevant enterprise information. The user model encodes the user's topics of interest and email processing behaviors (e.g. read/reply/file) at the granularity of pair-wise interactions between the user and each of his/her email contacts. Given a new message, the user model is used in combination with the message metadata and content to determine the values of a set of contextual features. Contextual features include people-centric features representing information about the user's interaction history and relationship with the email sender, as well as message-centric features focusing on the properties of the message itself. Based on these feature values, EPIC uses a dynamic strategy to combine a global priority classifier with a user-specific classifier for determining the message's priority. An evaluation of EPIC based on 2,064 annotated email messages from 11 users, using 10-fold cross-validation, showed that the system achieves an average accuracy of 81.3%. The user-specific classifier contributed an improvement of 11.5%. Lastly we report on findings regarding the relative value of different contextual features for email prioritization.

Reuse of existing presentation materials is prevalent among knowledge workers. However, finding the most appropriate material for reuse is challenging. Existing information management and search tools provide inadequate support for reuse due to their dependence on users' ability to effectively categorize, recall, and recognize existing materials. Based on our findings from an online survey and contextual interviews, we designed and implemented a slide-based contextual recommender, ConReP, for supporting reuse of presentation materials. ConReP utilizes a user-selected slide as a search-key, recommends materials based on similarity to the selected slide, and provides a local-context-based visual representation of the recommendations. Users input provides new insight into presentation reuse and reveals that slide-based search is more effective than keyword-based search, local-context-based visual representation helps in better recall and recognition, and shows the promise of this general approach of exploiting individual slides and local-context for better presentation reuse.

We present a large-scale study of content reuse networks in a large and highly hierarchical organization. In our study, we combine analysis of a collection of presentations produced by employees with interviews conducted throughout the organization and a survey to study presentation content reuse. Study results show a variety of information needs and behaviors related to content reuse as well as a need for a personalized and socially-integrated networking tool for enabling easy access to previously generated presentation material. In this paper we describe our findings and outline a set of requirements for an effective content reuse facility.

We present ICARUS, a contextual information retrieval system, which uses the current email message and a multi-tiered user model to retrieve relevant content and make it available in a sidebar widget embedded in the email client. The system employs a dynamic retrieval strategy to conduct automated contextual search across multiple information sources including the user's hard drive, online documents (wikis, blogs and files) and other email messages. It also presents the user with information about the sender of the current message, which varies in detail and degree based on how often the user interacts with this sender. We conducted a formative evaluation which compared three retrieval methods that used different context information: current message plus a multi-tiered user model; current message plus a single-tiered, aggregate user model; and lastly, cur-rent message only. Results indicate that the multi-tiered user modeling approach yields better retrieval performance than the other two. In addition, the study suggests that dynamically determining which sources to search, what query parameters to use, and how to filter/re-rank results can further improve the effectiveness of contextual IR.

We describe fieldwork in which we studied hospital ICU physicians and their strategies and documentation aids for composing patient progress notes. We then present a clinical documentation prototype, activeNotes, that supports the creation of these notes, using techniques designed based on our fieldwork. ActiveNotes integrates automated, context-sensitive patient data retrieval, and user control of automated data updates and alerts via tagging, into the documentation process. We performed a qualitative study of activeNotes with 15 physicians at the hospital to explore the utility of our information retrieval and tagging techniques. The physicians indicated their desire to use tags for a number of purposes, some of them extensions to what we intended, and others new to us and unexplored in other systems of which we are aware. We discuss the physicians' responses to our prototype and distill several of their proposed uses of tags: to assist in note content management, communication with other clinicians, and care delivery.

We present a system that supports seamless access to information contained in recorded meetings from the cornerstone points of a knowledge worker's daily life: mailbox and calendar. The solution supports granular search of meeting content from an enterprise email system and automatically displays recordings of meetings related to the message the user is currently viewing. Additionally thumbnail summaries of the meetings are added to the user's calendar entries after the meetings have taken place. Lastly our system supports easy sharing of videos associated with recorded meetings through the use of hot-linked thumbnail summaries which can be sent via email.

Presentation material is a commonly-performed task. Yet current tools provide inadequate support - search tools are unable to return individual slides, and the linear model employed by presentation creation tools lacks structure and context. We propose a novel method for presentation creation, implemented in a tool called Outline Wizard, which enables outline-based composition and search. An Outline Wizard user enters a hierarchically-structured outline of a presentation; using that structure, the tool extracts user requests to formulate contextual queries, matches them against presentations within a repository, taking into account both content and structures of the presentations, and presents the user with sets of slides that are appropriate for each outline topic. At the heart of Outline Wizard is an outline-based search technique, which conducts content search within the context derived from the hierarchical structures of both user requests and presentations. We present a heuristic outline-extraction technique, which is used to reverse engineer the structures of presentations, thereby making the structures available for our search engine. Evaluations show that the outline extraction technique and outline-based search both perform well, and that users report a satisfying experience when using Outline Wizard to compose presentations from libraries of existing material.

User modeling for information retrieval has mostly been studied to improve the effectiveness of information access in centralized repositories. In this paper we explore user modeling in the context of full-text federated search in peer-to-peer networks. Our approach models a user's persistent, long-term interests based on past queries, and uses the model to improve search efficiency for future queries that represent interests similar to past queries. Our approach also enables queries representing a user's transient, ad-hoc interests to be automatically recognized so that search for these queries can rely on a relatively large search radius to avoid sacrificing effectiveness for efficiency. Experimental results demonstrate that our approach can significantly improve the efficiency of full-text federated search without degrading its accuracy. Furthermore, the proposed approach does not require a large amount of training data, and is robust to a range of parameter values.

No abstract available.

Hybrid peer-to-peer architectures use special nodes to provide directory services for regions of the network ("regional directory services"). Hybrid peer-to-peer architectures are a potentially powerful model for developing large-scale networks of complex digital libraries, but peer-to-peer networks have so far tended to use very simple methods of resource selection and document retrieval. In this paper, we study the application of content-based resource selection and document retrieval to hybrid peer-to-peer networks. The directory nodes that provide regional directory services construct and use the content models of neighboring nodes to determine how to route query messages through the network. The leaf nodes that provide information use content-based retrieval to decide which documents to retrieve for queries. The experimental results demonstrate that using content-based retrieval in hybrid peer-to-peer networks is both more accurate and more efficient for some digital library environments than more common alternatives such as Gnutella 0.6.

Query-based sampling is a method of discovering the contents of a text database by submitting queries to a search engine and observing the documents returned. In prior research sampled documents were used to build resource descriptions for automatic database selection, and to build a centralized sample database for query expansion and result merging. An unstated assumption was that the associated storage costs were acceptable.When sampled documents are long, storage costs can be large. This paper investigates methods of pruning long documents to reduce storage costs. The experimental results demonstrate that building resource descriptions and centralized sample databases from the pruned contents of sampled documents can reduce storage costs by 54-93% while causing only minor losses in the accuracy of distributed information retrieval.