图书出版的搜索引擎营销策略

本文从出版社如何开展网络营销入手,引出搜索引擎营销这一利器,通过翔实、具体的例子阐明了出版社应该如何实施搜索引擎营销策略,详细介绍了搜索引擎导向的广告投放策略、搜索引擎导向的网站优化策略、搜索引擎导向的网站推广策略、搜索引擎导向的信息收集策略.     

防范科技论文一稿多投和重复发表的实践与思考

为了有效防范科技论文的一稿多投和重复发表,<第三军医大学学报>不断完善防范机制,制定了简便可行的鉴别标准和实施办法,采取加强作者教育、启用"学术不端文献检测系统"、合理使用对作者的制约手段等措施,并本着"以人为本,重在教育"的原则,妥善处理违规稿件和作者,取得了较好效果.     

Asymmetric Missing-data Problems: Overcoming the Lack of Negative Data in Preference Ranking

In certain classification problems there is a strong a asymmetry between the number of labeled examples available for each of the classes involved. In an extreme case, there may be a complete lack of labeled data for one of the classes while, at the same time, there are adequate labeled examples for the others, accompanied by a large body of unlabeled data. Since most classification algorithms require some information about all classes involved, label estimation for the un-represented class is desired. An important representative of this group of problems is that of user interest/preference modeling where there may be a large number of examples of what the user likes with essentially no counterexamples.Recently, there has been much interest in applying the EM algorithm to incomplete data problems in the area of text retrieval and categorization. We adapt this approach to the asymmetric case of modeling user interests in news articles, where only labeled positive training data are available, with access to a large corpus of unlabeled documents. User modeling is here equivalent to that of user-specific document ranking. EM is used in conjunction with the Naive Bayes model while its output is also utilized by a Support Vector Machine and Rocchio's technique.Our findings demonstrate that the EM algorithm can be quite effective in modeling the negative class under a number of different initialization schemes. Although primarily just the negative training examples are needed, a natural question is whether using all of the estimated labels (i.e., positive and negative) would be more (or less) beneficial. This is important considering that, in this context, the initialization of the negative class for EM is likely not to be very accurate. Experimental results suggest that EM output should be limited to negative label estimates only.     

Classifying documents with link-based bibliometric measures

Automatic document classification can be used to organize documents in a digital library, construct on-line directories, improve the precision of web searching, or help the interactions between user and search engines. In this paper we explore how linkage information inherent to different document collections can be used to enhance the effectiveness of classification algorithms. We have experimented with three link-based bibliometric measures, co-citation, bibliographic coupling and Amsler, on three different document collections: a digital library of computer science papers, a web directory and an on-line encyclopedia. Results show that both hyperlink and citation information can be used to learn reliable and effective classifiers based on a kNN classifier. In one of the test collections used, we obtained improvements of up to 69.8% of macro-averaged F ₁ over the traditional text-based kNN classifier, considered as the baseline measure in our experiments. We also present alternative ways of combining bibliometric based classifiers with text based classifiers. Finally, we conducted studies to analyze the situation in which the bibliometric-based classifiers failed and show that in such cases it is hard to reach consensus regarding the correct classes, even for human judges.     

OSGS—A Personalized Online Store for E-Commerce Environments

Current online stores suffer from a cardinal problem. There are too many products to offer, and customers find themselves lost due to the vast selection. As opposed to traditional stores, there is little or no guidance that helps the customers as they search. In this paper, we propose a new approach for designing a successful personalized online store enabling the successful searching of customers in the store. This approach is based on algorithms commonly used in recommendation systems, but which are rarely used for searches in online stores. We employ this approach for both keyword and browse searches, and present an implementation of this approach. We compared several search guide algorithms experimentally, and the experiments' results show that the suggested algorithms are applicable to the domain of online stores.     

用AUC评估分类器的预测性能

准确率一直被作为分类器预测性能的主要评估标准,但是它存在着诸多的缺点和不足。本文将准确率与AUC(the area under the Receiver Operating Characteristic curve)进行了理论上的对比分析,并分别使用AUC和准确率对3种分类学习算法在15个两类数据集上进行了评估。综合理论和实验两个方面的结果,显示了AUC不但优于而且应该替代准确率,成为更好的分类器性能的评估度量。同时,用AUC对3种分类学习算法的重新评估,进一步证实了基于贝叶斯定理的NaiveBayes和TAN-CMI分类算法优于决策树分类算法C4．5。     

A Review of "Using Tablets and Apps in Libraries"

Abstract

Academic libraries have increasingly adopted web-scale discovery tools in order to meet the expectations of students who want immediate results, efficient algorithms, and a single search box. As they have become the de facto search tool at many libraries, librarians and patrons alike have been forced to confront the way they think about research processes and in some cases, modulate ingrained habits. In this article, the authors explore student perceptions of efficacy, relevancy, and ease of use of a library discovery tool through a 2017 mixed-methods user experience study conducted at three City University of New York (CUNY) campuses. The results from this user study will be useful to other institutions that already have implemented discovery layers within their library and help inform website design, discovery layer design, and pedagogy. By combining student interviews with a task analysis, the authors were able to learn more about student search behaviors, how they respond to challenges, and what they expect from search tools. Identifying these barriers to student use of the Primo discovery tool has helped us articulate best practices for instruction and interface customization that may address these barriers and has illuminated implications for website integration.     

Insights and opinions of readers of the Journal of the Medical Library Association

The Journal of the Medical Library Association (JMLA) conducted a readership survey in 2020 to gain a deeper understanding of our readers, their reading habits, and their satisfaction with JMLA''s content, website functionality, and overall quality. A total of 467 readers responded to the survey, most of whom were librarians/information specialists (85%), worked in an academic (62%) or hospital/health care system (27%) library, and were current Medical Library Association members (80%). Most survey respondents (46%) reported reading JMLA articles on a quarterly basis. Over half of respondents (53%) said they used social media to follow new research or publications, with Twitter being the most popular platform. Respondents stated that Original Investigations, Case Reports, Knowledge Syntheses, and Resource Reviews articles were the most enjoyable to read and important to their research and practice. Almost all respondents reported being satisfied or very satisfied (94%) with the JMLA website. Some respondents felt that the content of JMLA leaned more toward academic librarianship than toward clinical/hospital librarianship and that there were not enough articles on collection management or technical services. These opinions and insights of our readers help keep the JMLA editorial team on track toward publishing articles that are of interest and utility to our audience, raising reader awareness of new content, providing a website that is easy to navigate and use, and maintaining our status as the premier journal in health sciences librarianship.

The Journal of the Medical Library Association (JMLA), in tandem with the Medical Library Association (MLA), periodically conducts readership surveys to gauge how well the journal meets the needs and desires of its readers. However, much time has passed and many changes have occurred since our last readership survey was published in 2013 [1]. These changes include our transition from a largely print-based to an almost fully digital mode of publication, more extensive reliance on providing access to supplemental materials to improve the rigor and reproducibility of research findings, and the use of social media to share and stay abreast of new scholarly works. Therefore, we conducted a readership survey in 2020 to obtain a more current understanding of our readers, their reading habits, and their satisfaction with JMLA''s content, website functionality, and overall quality.The survey was implemented using Qualtrics and consisted of sixteen closed- and open-ended questions (Appendix A). The survey invitation was distributed on July 6, 2020, through MLA email listservs and social media accounts (Twitter, Facebook, LinkedIn), JMLA''s Twitter account, and an announcement on the JMLA website. Recipients were encouraged to forward the invitation through other communication channels as appropriate. The survey was closed on July 20, 2020.     

NIHSeniorHealth: A Free Tool for Online Health Information for Older Adults

NIHSeniorHealth is a free, consumer health website that covers health topics affecting older adults. The website was created and is maintained by the National Library of Medicine (NLM) and features more than 55 health topics and nearly 150 videos. The easy-to-use navigational and visual tools create a user-friendly experience for older adults, their families, and caregivers who seek senior-specific information on the web. This column will include an overview of the website, a simple search, and a review of the features of NIHSeniorHealth.     

一稿多投的重灾区:中学教育类数学期刊

通过事例说明中学教育类数学期刊中一稿多投、一稿多发极为严重的现状,提出了合理、有效、可操作的对策。     

Finding a new kind of knowledge on the HeartNET website

Background: A website developed by the National Heart Foundation (WA Division) and Edith Cowan University, with the help of an Australian Research Council–Linkage grant, provides insight into the sense of isolation experienced by many heart patients which prompts them to engage in a relentless search to answer the fundamental question: why me? Objective: To discover whether an online community for people with heart conditions may help instil a sense of sharing a journey with others, and to assess the impact of this shared experience. Methods: The qualitative data constituted 50 in‐depth interviews with heart patients using the HeartNET website. This website, with its 600+ membership, also provides Discussion Board data to add depth to the analysis. Results: Patients describe how their unsatisfactory search for information in one ‘place’ (the Internet) led them to discover a new ‘place’ (an online community) where they could ‘ask difficult questions’, and ‘gain support and wisdom’ from others. Conclusion: This paper suggests that, when anxious patients seek health‐related information, for example in a library, they may benefit from being given contact points to communicate with others who find themselves in similar situations. Internet‐based social software (Web 2.0) can provide this kind of communication.     

Partial Collection Replication for Information Retrieval

The explosion of content in distributed information retrieval (IR) systems requires new mechanisms in order to attain timely and accurate retrieval of unstructured text. This paper shows how to exploit locality by building, using, and searching partial replicas of text collections in a distributed IR system. In this work, a partial replica includes a subset of the documents from larger collection(s) and the corresponding inference network search mechanism. For each query, the distributed system determines if partial replica is a good match and then searches it, or it searches the original collection. We demonstrate the scenarios where partial replication performs better than systems that use caches which only store previous query and answer pairs. We first use logs from THOMAS and Excite to examine query locality using query similarity versus exact match. We show that searching replicas can improve locality (from 3 to 19%) over the exact match required by caching. Replicas increase locality because they satisfy queries which are distinct but return the same or very similar answers. We then present a novel inference network replica selection function. We vary its parameters and compare it to previous collection selection functions, demonstrating a configuration that directs most of the appropriate queries to replicas in a replica hierarchy. We then explore the performance of partial replication in a distributed IR system. We compare it with caching and partitioning. Our validated simulator shows that the increases in locality due to replication make it preferable to caching alone, and that even a small increase of 4% in locality translates into a performance advantage. We also show a hybrid system with caches and replicas that performs better than each on their own.     

Evaluating multimodal relevance feedback techniques for medical image retrieval

Medical image retrieval can assist physicians in finding information supporting their diagnosis and fulfilling information needs. Systems that allow searching for medical images need to provide tools for quick and easy navigation and query refinement as the time available for information search is often short. Relevance feedback is a powerful tool in information retrieval. This study evaluates relevance feedback techniques with regard to the content they use. A novel relevance feedback technique that uses both text and visual information of the results is proposed. The two information modalities from the image examples are fused either at the feature level using the Rocchio algorithm or at the query list fusion step using a common late fusion rule. Results using the ImageCLEF 2012 benchmark database for medical image retrieval show the potential of relevance feedback techniques in medical image retrieval. The mean average precision (mAP) is used as the evaluation metric and the proposed method outperforms commonly-used methods. The baseline without feedback reached 16 % whereas the relevance feedback with 20 images reached up to 26.35 % with three steps and when using 100 images up to 34.87 % in four steps. Most improvements occur in the first two steps of relevance feedback and then results start to become relatively flat. This might also be due to only using positive feedback as negative feeback often also improves results after more steps. The effect of relevance feedback in automatically spelling corrected and translated queries is investigated as well. Results without mistakes were better than spell-corrected results but the spelling correction more than double results over non-corrected retrieval. Multimodal relevance feedback has shown to be able to help visual medical information retrieval. Next steps include integrating semantics into relevance feedback techniques to benefit from the structured knowledge of ontologies and experimenting on the fusion of text and visual information.     

企业网站链接指标与企业信息化及绩效之间的相关性研究

以2008年中国企业信息化500强中的企业作为研究对象,通过Altavista获取其中100家企业门户网站的链接指标,对网站链接指标数据与信息化排名进行相关性分析,发现企业的信息化水平存在较大差距时,网站链接指标与信息化排名显著相关;通过搜索引擎获取其中10家上市企业的绩效指标数据,对网站链接指标数据和绩效指标数据进行相关性分析,发现企业绩效指标与其门户网站的链接总数和外部链接数显著相关。     

电子商务网站信息构建研究

首先简要对信息构建(IA)进行概述;然后根据成本-收益范式,从浏览系统、搜索系统和商品展示页面等三个方面对常见大型电子商务网站信息构建的有效性进行分析,并针对其不足提出改进策略;最后提出了一个电子商务网站系统(cyskmall)的IA设计方案。     

基于日志挖掘的用户健康信息检索行为研究

[目的/意义] 针对当前我国网络用户的健康信息检索行为, 探索利用中文搜索引擎的健康信息检索规律, 为完善健康搜索引擎和网站建设提供参考。[方法/过程] 基于搜狗搜索引擎的大规模查询日志, 采用日志挖掘的方法, 从查询行为和点击行为两个角度对网络用户的健康信息检索行为进行研究。查询行为的研究指标包括会话层(会话长度、用户重复查询), 查询串层(查询串长度、重复查询)和词项层(高频词汇, 主题分类);点击行为的研究指标为点击位置和点击内容。[结果/结论] 健康相关查询的重复率较高, 提示相关网站可缓存高重复率查询串的返回结果;大众关注的热点领域为疾病、保健、母婴、医疗机构与美容整形, 提示网站的导航设计注意导航方向;用户更偏爱使用问答型平台, 提示网站设计者应更加关注与用户间问答型的互动模式。     

图书馆数字化资源整合的现状透视

本文以各图书馆的网站信息为主要数据来源，对国内采用了不同整合平台进行数字化资源整合的10家图书馆，从资源整合现状、整合方式、整合的资源类型、检索功能、检索结果、用户管理功能等几个方面展开对我国数字化资源整合的现状分析和评价，并针对问题的症结提出了建设性意见，为我国数字化资源整合的发展提供一定的理论指导。     

基于深度学习算法的学术查询意图分类器构建

[目的/意义]实现学术查询意图的自动识别,提高学术搜索引擎的效率。[方法/过程]结合已有查询意图特征和学术搜索特点,从基本信息、特定关键词、实体和出现频率4个层面对查询表达式进行特征构造,运用Naive Bayes、Logistic回归、SVM、Random Forest四种分类算法进行查询意图自动识别的预实验,计算不同方法的准确率、召回率和F值。提出了一种将Logistic回归算法所预测的识别结果扩展到大规模数据集、提取"关键词类"特征的方法构建学术查询意图识别的深度学习两层分类器。[结果/结论]两层分类器的宏平均F1值为0.651,优于其他算法,能够有效平衡不同学术查询意图的类别准确率与召回率效果。两层分类器在学术探索类的效果最好,F1值为0.783。     

避免科技论文重复发表和一稿多投的机制与实践

针对科技期刊界论文重复发表和一稿多投问题较为突出的情况,建立了在稿件送同行专家评审前进行查重和在稿件刊登前再查重的机制.经过1年多的实践,避免了60余篇属重复发表或有抄袭嫌疑稿件的发表,并对一稿多投有所抑制.