2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL)最新文献_第8页

Semantometrics: Towards fulltext-based research evaluation 语义计量学:迈向基于全文的研究评价

2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL) Pub Date : 2016-05-13 DOI: 10.1145/2910896.2925448

Drahomira Herrmannova, Petr Knoth

引用次数: 4

A supervised learning algorithm for binary domain classification of Web queries using SERPs 使用serp对Web查询进行二元域分类的监督学习算法

2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL) Pub Date : 2016-04-30 DOI: 10.1145/2910896.2925449

Alexander C. Nwala, Michael L. Nelson

{"title":"A supervised learning algorithm for binary domain classification of Web queries using SERPs","authors":"Alexander C. Nwala, Michael L. Nelson","doi":"10.1145/2910896.2925449","DOIUrl":"https://doi.org/10.1145/2910896.2925449","url":null,"abstract":"General purpose Search Engines (SEs) crawl all domains (e.g., Sports, News, Entertainment) of the Web, but sometimes the informational need of a query is restricted to a particular domain (e.g., Medical). We leverage the work of SEs as part of our effort to route domain specific queries to local Digital Libraries (DLs). SEs are often used even if they are not the “best” source for certain types of queries. Rather than tell users to “use this DL for this kind of query”, we intend to automatically detect when a query could be better served by a local DL (such as a private, access-controlled DL that is not crawlable via SEs). This is not an easy task because Web queries are short, ambiguous, and there is lack of quality labeled training data (or it is expensive to create). To detect queries that should be routed to local, specialized DLs, we first send the queries to Google and then examine the features in the resulting Search Engine Result Pages. Using 400,000 AOL queries for the “non-scholar” domain and 400,000 queries from the NASA Technical Report Server for the “scholar” domain, our classifier achieved a precision of 0.809 and F-measure of 0.805.","PeriodicalId":109613,"journal":{"name":"2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115666606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Profiling vs. time vs. content: What does matter for top-k publication recommendation based on Twitter profiles? 简介vs时间vs内容:基于Twitter简介的top-k出版物推荐中，什么重要?

2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL) Pub Date : 2016-03-22 DOI: 10.1145/2910896.2910898

Chifumi Nishioka, A. Scherp

{"title":"Profiling vs. time vs. content: What does matter for top-k publication recommendation based on Twitter profiles?","authors":"Chifumi Nishioka, A. Scherp","doi":"10.1145/2910896.2910898","DOIUrl":"https://doi.org/10.1145/2910896.2910898","url":null,"abstract":"So far it is unclear how different factors of a scientific publication recommender system based on users' tweets have an influence on the recommendation performance. We examine three different factors, namely profiling method, temporal decay, and richness of content. Regarding profiling, we compare CF-IDF that replaces terms in TF-IDF by semantic concepts, HCF-IDF as novel hierarchical variant of CF-IDF, and topic modeling. As temporal decay functions, we apply sliding window and exponential decay. In terms of the richness of content, we compare recommendations using both full-texts and titles of publications and using only titles. Overall, the three factors make twelve recommendation strategies. We have conducted an online experiment with 123 participants and compared the strategies in a within-group design. The best recommendations are achieved by the strategy combining CF-IDF, sliding window, and with full-texts. However, the strategies using the novel HCF-IDF profiling method achieve similar results with just using the titles of the publications. Therefore, HCF-IDF can make recommendations when only short and sparse data is available.","PeriodicalId":109613,"journal":{"name":"2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121913481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 30

Introduction to the Digital Public Library of America API 美国数字公共图书馆API简介

2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL) Pub Date : 1900-01-01 DOI: 10.1109/jcdl.2017.7991621

Unmil Karadkar, Audrey Altman, M. Breedlove, María A. Matienzo

引用次数: 0