Personalized online information search and visualization

BACKGROUND: The rapid growth of online publications such as the Medline and other sources raises the questions how to get the relevant information efficiently. It is important, for a bench scientist, e.g., to monitor related publications constantly. It is also important, for a clinician, e.g., to access the patient records anywhere and anytime. Although time-consuming, this kind of searching procedure is usually similar and simple. Likely, it involves a search engine and a visualization interface. Different words or combination reflects different research topics. The objective of this study is to automate this tedious procedure by recording those words/terms in a database and online sources, and use the information for an automated search and retrieval. The retrieved information will be available anytime and anywhere through a secure web server. RESULTS: We developed such a database that stored searching terms, journals and et al., and implement a piece of software for searching the medical subject heading-indexed sources such as the Medline and other online sources automatically. The returned information were stored locally, as is, on a server and visible through a Web-based interface. The search was performed daily or otherwise scheduled and the users logon to the website anytime without typing any words. The system has potentials to retrieve similarly from non-medical subject heading-indexed literature or a privileged information source such as a clinical information system. The issues such as security, presentation and visualization of the retrieved information were thus addressed. One of the presentation issues such as wireless access was also experimented. A user survey showed that the personalized online searches saved time and increased and relevancy. Handheld devices could also be used to access the stored information but less satisfactory. CONCLUSION: The Web-searching software or similar system has potential to be an efficient tool for both bench scientists and clinicians for their daily information needs

SocialSensor: sensing user generated input for improved media discovery and experience

SocialSensor will develop a new framework for enabling real-time multimedia indexing and search in the Social Web. The project moves beyond conventional text-based indexing and retrieval models by mining and aggregating user inputs and content over multiple social networking sites. Social Indexing will incorporate information about the structure and activity of the users‟ social network directly into the multimedia analysis and search process. Furthermore, it will enhance the multimedia consumption experience by developing novel user-centric media visualization and browsing paradigms. For example, SocialSensor will analyse the dynamic and massive user contributions in order to extract unbiased trending topics and events and will use social connections for improved recommendations. To achieve its objectives, SocialSensor introduces the concept of Dynamic Social COntainers (DySCOs), a new layer of online multimedia content organisation with particular emphasis on the real-time, social and contextual nature of content and information consumption. Through the proposed DySCOs-centered media search, SocialSensor will integrate social content mining, search and intelligent presentation in a personalized, context and network-aware way, based on aggregation and indexing of both UGC and multimedia Web content

The contribution of data mining to information science

The information explosion is a serious challenge for current information institutions. On the other hand, data mining, which is the search for valuable information in large volumes of data, is one of the solutions to face this challenge. In the past several years, data mining has made a significant contribution to the field of information science. This paper examines the impact of data mining by reviewing existing applications, including personalized environments, electronic commerce, and search engines. For these three types of application, how data mining can enhance their functions is discussed. The reader of this paper is expected to get an overview of the state of the art research associated with these applications. Furthermore, we identify the limitations of current work and raise several directions for future research

Computer-based library or computer-based learning?

Traditionally, libraries have played the role of repository of published information resources and, more recently, gateway to online subscription databases. The library online catalog and digital library interface serve an intermediary function to help users locate information resources available through the library. With competition from Web search engines and Web portals of various kinds available for free, the library has to step up to play a more active role as guide and coach to help users make use of information resources for learning or to accomplish particular tasks. It is no longer sufficient for computer-based library systems to provide just search and access functions. They must provide the functionality and environment to support learning and become computer-based learning systems. This paper examines the kind of learning support that can be incorporated in library online catalogs and digital libraries, including 1) enhanced support for information browsing and synthesis through linking by shared meta-data, references and concepts; 2) visualization of related information; 3) adoption of Library 2.0 and social technologies; 4) adoption of Library 3.0 technologies including intelligent processing and text mining

Neural‑Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding

Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have attracted considerable interest in recent years. Existing works have demonstrated that vertex representation learned through an embedding method provides superior performance in many real-world applications, such as node classification, link prediction, and community detection. However, most of the existing methods for network embedding only utilize topological information of a vertex, ignoring a rich set of nodal attributes (such as user profiles of an online social network, or textual contents of a citation network), which is abundant in all real-life networks. A joint network embedding that takes into account both attributional and relational information entails a complete network information and could further enrich the learned vector representations. In this work, we present Neural-Brane, a novel Neural Bayesian Personalized Ranking based Attributed Network Embedding. For a given network, Neural-Brane extracts latent feature representation of its vertices using a designed neural network model that unifies network topological information and nodal attributes. Besides, it utilizes Bayesian personalized ranking objective, which exploits the proximity ordering between a similar node pair and a dissimilar node pair. We evaluate the quality of vertex embedding produced by Neural-Brane by solving the node classification and clustering tasks on four real-world datasets. Experimental results demonstrate the superiority of our proposed method over the state-of-the-art existing methods

Visualizing recommendations to support exploration, transparency and controllability

Research on recommender systems has traditionally focused on the development of algorithms to improve accuracy of recommendations. So far, little research has been done to enable user interaction with such systems as a basis to support exploration and control by end users. In this paper, we present our research on the use of information visualization techniques to interact with recommender systems. We investigated how information visualization can improve user understanding of the typically black-box rationale behind recommendations in order to increase their perceived relevance and meaning and to support exploration and user involvement in the recommendation process. Our study has been performed using TalkExplorer, an interactive visualization tool developed for attendees of academic conferences. The results of user studies performed at two conferences allowed us to obtain interesting insights to enhance user interfaces that integrate recommendation technology. More specifically, effectiveness and probability of item selection both increase when users are able to explore and interrelate multiple entities - i.e. items bookmarked by users, recommendations and tags. Copyright © 2013 ACM