SWM '17最新文献 - Book学术

Describing Data Processing Pipelines in Scientific Publications for Big Data Injection 面向大数据注入的科学出版物数据处理管道描述

SWM '17 Pub Date : 2017-02-10 DOI: 10.1145/3057148.3057149

S. Mesbah, A. Bozzon, C. Lofi, G. Houben

引用次数: 6

Scalable Algorithm for Probabilistic Overlapping Community Detection 概率重叠社区检测的可扩展算法

SWM '17 Pub Date : 2017-02-10 DOI: 10.1145/3057148.3057150

Kento Nozawa, Kei Wakabayashi

引用次数: 0

Citations and Readership are Poor Indicators of Research Excellence: Introducing TrueImpactDataset, a New Dataset for Validating Research Evaluation Metrics 引用和读者是研究卓越性的不良指标:介绍TrueImpactDataset，一个验证研究评估指标的新数据集

SWM '17 Pub Date : 2017-02-10 DOI: 10.1145/3057148.3057154

Drahomira Herrmannova, R. Patton, Petr Knoth, Christopher G. Stahl

引用次数: 7

ScholarBase: Towards a Cross-Domain Knowledgebase for Linked Scholarly Data ScholarBase:面向链接学术数据的跨领域知识库

SWM '17 Pub Date : 2017-02-10 DOI: 10.1145/3057148.3057153

Mahmoud Elbattah

引用次数: 0

Building recommender systems for scholarly information 建立学术信息推荐系统

SWM '17 Pub Date : 2017-02-10 DOI: 10.1145/3057148.3057152

Maya Hristakeva, Daniel James Kershaw, Marco Rossetti, Petr Knoth, Benjamin Pettit, S. Vargas, Kris Jack

{"title":"Building recommender systems for scholarly information","authors":"Maya Hristakeva, Daniel James Kershaw, Marco Rossetti, Petr Knoth, Benjamin Pettit, S. Vargas, Kris Jack","doi":"10.1145/3057148.3057152","DOIUrl":"https://doi.org/10.1145/3057148.3057152","url":null,"abstract":"The depth and breadth of research now being published is overwhelming for an individual researcher to keep track of let alone consume. Recommender systems have been developed to make it easier for researchers to discover relevant content. However, these have predominately taken the form of item-to-item recommendations using citation network features or text similarity features.\u0000 This paper details how the Mendeley Suggest recommender system has been designed and developed. We show how implicit user feedback (based on activity data from the reference manager) and collaborative filtering (CF) are used to generate the recommendations for Mendeley Suggest. Because collaborative filtering suffers from the cold start problem (the inability to serve recommendations to new users), we developed additional recommendation methods based on user-defined attributes, such as discipline and research interests.\u0000 Our off-line evaluation shows that where possible, recommendations based on collaborative filtering perform best, followed by recommendations based on recent activity. However, for cold users (for whom collaborative filtering was not possible) recommendations based on discipline performed best. Additionally, when we segmented users by career stages, we found that among senior academics, content-based recommendations from recent activity had comparable performance to collaborative filtering. This justifies our approach of developing a variety of recommendation methods, in order to serve a range of users across the academic spectrum.","PeriodicalId":338752,"journal":{"name":"SWM '17","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123434919","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: 22

Effectively identifying users' research interests for scholarly reference management and discovery 有效识别用户的研究兴趣，进行学术参考资料管理和发现

SWM '17 Pub Date : 2017-02-10 DOI: 10.1145/3057148.3057151

Marco Rossetti, S. Vargas, Davide Magatti, Benjamin Pettit, Daniel James Kershaw, Maya Hristakeva, Kris Jack

{"title":"Effectively identifying users' research interests for scholarly reference management and discovery","authors":"Marco Rossetti, S. Vargas, Davide Magatti, Benjamin Pettit, Daniel James Kershaw, Maya Hristakeva, Kris Jack","doi":"10.1145/3057148.3057151","DOIUrl":"https://doi.org/10.1145/3057148.3057151","url":null,"abstract":"Discovering users' interests is essential in order to help them explore resources in large digital repositories. In particular, correctly identifying users' interests is commonly a good approach for organising information and providing personalised recommendations. We consider here the case of discovering users' research interests in Mendeley a research platform for scholarly article management and discovery. Prior work in this area has considered approaches such as matrix factorisation and text-based topic modelling for inferring topics of interest in recommendation scenarios. These approaches present several problems, such as little or no interpretability of the inferred topics and difficulty handling similarities in vocabulary in different research disciplines. We present an effective solution for extracting coherent and interpretable research topics that leverages the reference management data in Mendeley in a three-step approach: 1) a topic model based on the interactions between users and articles rather than article content, 2) keyword extraction to label the topics using article titles and author-declared keywords and 3) identifying the research interests of users based on the articles that they have added to their libraries. An evaluation comprised of a research interest prediction task and an article recommendation task shows the validity of our proposal in different research disciplines (clearly outperforming a text-based latent topic model) and provides further insights regarding the effects of number of latent topics in the model and the trade-off between recency and quantity of the users' libraries.","PeriodicalId":338752,"journal":{"name":"SWM '17","volume":"194 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125863259","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: 1

Linking Mathematical Expressions to Wikipedia 链接数学表达式到维基百科

SWM '17 Pub Date : 2017-02-10 DOI: 10.1145/3057148.3057156

Giovanni Yoko Kristianto, Akiko Aizawa

引用次数: 5