How users search and what they search for in the medical domain

The internet is an important source of medical knowledge for everyone, from laypeople to medical professionals. We investigate how these two extremes, in terms of user groups, have distinct needs and exhibit significantly different search behaviour. We make use of query logs in order to study various aspects of these two kinds of users. The logs from America Online, Health on the Net, Turning Research Into Practice and American Roentgen Ray Society (ARRS) GoldMiner were divided into three sets: (1) laypeople, (2) medical professionals (such as physicians or nurses) searching for health content and (3) users not seeking health advice. Several analyses are made focusing on discovering how users search and what they are most interested in. One possible outcome of our analysis is a classifier to infer user expertise, which was built. We show the results and analyse the feature set used to infer expertise. We conclude that medical experts are more persistent, interacting more with the search engine. Also, our study reveals that, conversely to what is stated in much of the literature, the main focus of users, both laypeople and professionals, is on disease rather than symptoms. The results of this article, especially through the classifier built, could be used to detect specific user groups and then adapt search results to the user group.     

Enhancing click models with mouse movement information

User interactions in Web search, in particular, clicks, provide valuable hints on document relevance; but the signals are very noisy. In order to better understand user click behaviors and to infer the implied relevance, various click models have been proposed, each relying on some hypotheses and involving different hidden events (e.g. examination). In almost all the existing click models, it is assumed that clicks are the only observable evidence and the examinations of documents are deduced from it. However, with an increasing number of embedded heterogeneous components (e.g. verticals) on Search Engine Result Pages, click information is not sufficient to draw a complete picture of process of user examination, especially in federated search scenario. In practice, we can also collect mouse movement information, which has proven to have a strong correlation with examination. In this paper, we propose to incorporate mouse movement information into existing click models to enhance the estimation of examination. The enhanced click models are shown to have a better ability to predict both user clicks and document relevance, than the original models. The collection of mouse movement information has been implemented in a commercial search engine, showing the feasibility of the approach in practice.     

Online library catalog search performance by older adult users

Previous information retrieval research has neglected examination of older adults' search behavior. The present study examined the online search performance and associated errors of 54 older adult users of a representative online library catalog system. Participants' error data were examined in the framework of Borgman's three layers of knowledge needed to perform online library catalog searching: (1) conceptual knowledge, (2) semantic knowledge, and (3) technical skills in executing the query. The results of older adults were consistent with findings of previous online library catalog studies with younger adults. Older adults experienced serious problems at all three knowledge layers but experienced their most severe difficulties with conceptual knowledge of how to search the online library catalog system (e.g., formulating queries, understanding and using keyword and Boolean searching, and understanding the contents of databases). The significance of these findings for older adults' use of online library catalogs will be discussed.     

Discriminative probabilistic models for expert search in heterogeneous information sources

In many realistic settings of expert finding, the evidence for expertise often comes from heterogeneous knowledge sources. As some sources tend to be more reliable and indicative than the others, different information sources need to receive different weights to reflect their degrees of importance. However, most previous studies in expert finding did not differentiate data sources, which may lead to unsatisfactory performance in the settings where the heterogeneity of data sources is present. In this paper, we investigate how to merge and weight heterogeneous knowledge sources in the context of expert finding. A relevance-based supervised learning framework is presented to learn the combination weights from training data. Beyond just learning a fixed combination strategy for all the queries and experts, we propose a series of discriminative probabilistic models which have increasing capability to associate the combination weights with specific experts and queries. In the last (and also the most sophisticated) proposed model, the combination weights depend on both expert classes and query topics, and these classes/topics are derived from expert and query features. Compared with expert and query independent combination methods, the proposed combination strategy can better adjust to different types of experts and queries. In consequence, the model yields much flexibility of combining data sources when dealing with a broad range of expertise areas and a large variation in experts. To the best of our knowledge, this is the first work that designs discriminative learning models to rank experts. Empirical studies on two real world faculty expertise testbeds demonstrate the effectiveness and robustness of the proposed discriminative learning models.     

Modeling actions of PubMed users with n-gram language models

Transaction logs from online search engines are valuable for two reasons: First, they provide insight into human information-seeking behavior. Second, log data can be used to train user models, which can then be applied to improve retrieval systems. This article presents a study of logs from PubMed^®, the public gateway to the MEDLINE^® database of bibliographic records from the medical and biomedical primary literature. Unlike most previous studies on general Web search, our work examines user activities with a highly-specialized search engine. We encode user actions as string sequences and model these sequences using n-gram language models. The models are evaluated in terms of perplexity and in a sequence prediction task. They help us better understand how PubMed users search for information and provide an enabler for improving users’ search experience.     

To search or to browse: How users navigate a new interface for online library tutorials

As academic libraries create online learning objects, it is important to consider whether such resources actually reach and answer the questions of intended users. This study considers three points of inquiry for one academic library: How its users make their way to the library's tutorials; user preference for searching or browsing for resources, when given both modes of access; and the kinds of online learning objects or tutorials users are seeking. A close examination of Web analytics and users’ search terms within the tutorials interface helped to illustrate patterns of access and highlighted users’ needs for tutorials and online learning objects.     

Re-ranking search results using language models of query-specific clusters

To obtain high precision at top ranks by a search performed in response to a query, researchers have proposed a cluster-based re-ranking paradigm: clustering an initial list of documents that are the most highly ranked by some initial search, and using information induced from these (often called) query-specific clusters for re-ranking the list. However, results concerning the effectiveness of various automatic cluster-based re-ranking methods have been inconclusive. We show that using query-specific clusters for automatic re-ranking of top-retrieved documents is effective with several methods in which clusters play different roles, among which is the smoothing of document language models. We do so by adapting previously-proposed cluster-based retrieval approaches, which are based on (static) query-independent clusters for ranking all documents in a corpus, to the re-ranking setting wherein clusters are query-specific. The best performing method that we develop outperforms both the initial document-based ranking and some previously proposed cluster-based re-ranking approaches; furthermore, this algorithm consistently outperforms a state-of-the-art pseudo-feedback-based approach. In further exploration we study the performance of cluster-based smoothing methods for re-ranking with various (soft and hard) clustering algorithms, and demonstrate the importance of clusters in providing context from the initial list through a comparison to using single documents to this end.

Project ISLA: A space/time/full-text/format search and retrieval system designed by end users

This article discusses the role of end users in the design of a multi-format digital library project (ISLA) being developed at the University of Southern California Library for the study of the Los Angeles region. ISLA focuses on the specific needs of cross-disciplinary research involving large urban regions.     

面向高校图书馆用户需求的层次化信息服务

文章在分析高校图书馆用户层次化信息需求特征的基础上,从基础性信息服务和深层次信息服务两方面论述了高校图书馆的层次化信息服务,并对开展层次化信息服务的途径和措施进行了探讨.     

MEDLINE and MeSH: challenges for end users

Through casual observations, formal consultations, and educational sessions, the authors have identified various indexing features of the National Library of Medicine's Medical Subject Headings (MeSH) that pose challenges to end users while attempting to obtain relevant retrieval when searching MEDLINE. These problematic features include the use of Explodes, Tree structures, subheadings, Text Word vs. subject heading searching, and central concept searching. End-user search software is becoming more sophisticated with an increasing number of choices offered for search strategy formulation. Methods of instruction to orient the end user to these systems will also have to become more detailed. A review of the literature, that discusses end-user problems with using MEDLINE and MeSH, is included.     

Reranking web search results for diversity

Search engine results are often biased towards a certain aspect of a query or towards a certain meaning for ambiguous query terms. Diversification of search results offers a way to supply the user with a better balanced result set increasing the probability that a user finds at least one document suiting her information need. In this paper, we present a reranking approach based on minimizing variance of Web search results to improve topic coverage in the top-k results. We investigate two different document representations as the basis for reranking. Smoothed language models and topic models derived by Latent Dirichlet?allocation. To evaluate our approach we selected 240 queries from Wikipedia disambiguation pages. This provides us with ambiguous queries together with a community generated balanced representation of their (sub)topics. For these queries we crawled two major commercial search engines. In addition, we present a new evaluation strategy based on Kullback-Leibler divergence and Wikipedia. We evaluate this method using the TREC sub-topic evaluation on the one hand, and manually annotated query results on the other hand. Our results show that minimizing variance in search results by reranking relevant pages significantly improves topic coverage in the top-k results with respect to Wikipedia, and gives a good overview of the overall search result. Moreover, latent topic models achieve competitive diversification with significantly less reranking. Finally, our evaluation reveals that our automatic evaluation strategy using Kullback-Leibler divergence correlates well with α-nDCG scores used in manual evaluation efforts.     

Criteria for evaluating alternative MEDLINE search engines

With so many options available for searching MEDLINE on the World Wide Web or as a component of an online service, evaluation criteria are suggested as a means of assisting librarians in determining the positive and negative aspects of alternative MEDLINE sites. A set of searches was utilized to systematically compare MEDLINE sites. Sites evaluated included Avicenna, America Online, HealthGate, PubMed, Medscape, and Physicians' Online. Some features used to evaluate these sites were: default fields; operators (default); access to MeSH; subheadings; stop words protected in MeSH; truncation; and stemming. This article will describe the group process used to arrive at the evaluation criteria, as well as some general conclusions which will help librarians in directing their users to a particular MEDLINE site.     

Television reviewing: A search for criteria

New communication technologies have increased Europe's importation of foreign, especially U.S., broadcasting programs. This essay addresses the question and protection of cultural identity from a European perspective. Four subgoals of diversity are outlined as yardsticks for the appropriateness of protection. Various activities designed to deregulate European broadcasting may potentially increase or reduce diversity. The economic and political pressure to weaken the European trusteeship model of broadcasting is likely to be successful.     

图书馆绿色阅览环境的营造与构建

本着“以人为本”的原则，从图书馆绿色阅览环境的构成要素分析入手，包括：绿色建材选用、室内空间及家具设计、绿色植物的选择与培育、绿色信息（知识）资源采集与传播以及背景音乐氛围的营造等，以现代绿色理念为指导，旨在系统全面推进图书馆阅览环境的建设。     

Optimizing search results for human learning goals

While past research has shown that learning outcomes can be influenced by the amount of effort students invest during the learning process, there has been little research into this question for scenarios where people use search engines to learn. In fact, learning-related tasks represent a significant fraction of the time users spend using Web search, so methods for evaluating and optimizing search engines to maximize learning are likely to have broad impact. Thus, we introduce and evaluate a retrieval algorithm designed to maximize educational utility for a vocabulary learning task, in which users learn a set of important keywords for a given topic by reading representative documents on diverse aspects of the topic. Using a crowdsourced pilot study, we compare the learning outcomes of users across four conditions corresponding to rankings that optimize for different levels of keyword density. We find that adding keyword density to the retrieval objective gave significant learning gains on some topics, with higher levels of keyword density generally corresponding to more time spent reading per word, and stronger learning gains per word read. We conclude that our approach to optimizing search ranking for educational utility leads to retrieved document sets that ultimately may result in more efficient learning of important concepts.     

计算机信息网络建设应遵循的几项原则

针对我国计算机信息网络的快速发展,阐述计算机网络建设应遵循的系统性、地区性、适应性、经济性、综合性和实用性等原则。