258,415 research outputs found

    Using chained machine learning models for scientific articles recommendation

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    Recommender systems are commonly used when it comes to online multimedia service providers or worldwide retail companies. Although, regarding educational resources, scientific papers and books, or other items with extensive textual content and description, recommendation systems are only in early development. In this paper, we propose a new approach entirely based on chained machine learning model store present and rank scientific papers. The first model a word embeddings model supported on a shallow neural network - is trained using a synthesized paper unit - a composition of the title, the abstract, the publishing conference or journal, and the year - that accurately captures paper’s semantic information. Later we train pairwise learning to a rank model based on a support vector machine (SVM) using relevant and irrelevant papers. We show that our approach achieves state-of-art results and does not rely on any language dependent or domain knowledge. It only uses available on-line data and proves to be efficient in either user-dependent and user independent modeling.info:eu-repo/semantics/acceptedVersio

    Injury risk factors, screening tests and preventative strategies: A systematic review of the evidence that underpins the perceptions and practices of 44 football (soccer) teams from various premier leagues

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    Purpose: To systematically review the scientific level of evidence for the ‘Top 3’ risk factors, screening tests and preventative exercises identified by a previously published survey of 44 premier league football (soccer) teams. Also, to provide an overall scientific level of evidence and graded recommendation based on the current research literature. Methods: A systematic literature search (Pubmed [MEDLINE], SportDiscus, PEDRO and Cochrane databases). The quality of the articles was assessed and a level of evidence (1++ to 4) was assigned. Level 1++ corresponded to the highest level of evidence available and 4, the lowest. A graded recommendation (A: strong, B: moderate, C: weak, D: insufficient evidence to assign a specific recommendation) for use in the practical setting was given. Results: Fourteen studies were analysed. The overall level of evidence for the risk factors previous injury, fatigue and muscle imbalance were 2++, 4 and ‘inconclusive’, respectively. The graded recommendation for functional movement screen, psychological questionnaire and isokinetic muscle testing were all ‘D’. Hamstring eccentric had a weak graded ‘C’ recommendation, and eccentric exercise for other body parts was ‘D’. Balance/proprioception exercise to reduce ankle and knee sprain injury was assigned a graded recommendation ‘D’. Conclusions: The majority of perceptions and practices of premier league teams have a low level of evidence and low graded recommendation. This does not imply that these perceptions and practices are not important or not valid, as it may simply be that they are yet to be sufficiently validated or refuted by research

    SciRecSys: A Recommendation System for Scientific Publication by Discovering Keyword Relationships

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    In this work, we propose a new approach for discovering various relationships among keywords over the scientific publications based on a Markov Chain model. It is an important problem since keywords are the basic elements for representing abstract objects such as documents, user profiles, topics and many things else. Our model is very effective since it combines four important factors in scientific publications: content, publicity, impact and randomness. Particularly, a recommendation system (called SciRecSys) has been presented to support users to efficiently find out relevant articles

    Citation recommendation: approaches and datasets

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    Citation recommendation describes the task of recommending citations for a given text. Due to the overload of published scientific works in recent years on the one hand, and the need to cite the most appropriate publications when writing scientific texts on the other hand, citation recommendation has emerged as an important research topic. In recent years, several approaches and evaluation data sets have been presented. However, to the best of our knowledge, no literature survey has been conducted explicitly on citation recommendation. In this article, we give a thorough introduction to automatic citation recommendation research. We then present an overview of the approaches and data sets for citation recommendation and identify differences and commonalities using various dimensions. Last but not least, we shed light on the evaluation methods and outline general challenges in the evaluation and how to meet them. We restrict ourselves to citation recommendation for scientific publications, as this document type has been studied the most in this area. However, many of the observations and discussions included in this survey are also applicable to other types of text, such as news articles and encyclopedic articles

    SCIENTIFIC ARTICLES RECOMMENDATION SYSTEM BASED ON USER’S RELATEDNESS USING ITEM-BASED COLLABORATIVE FILTERING METHOD

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    Scientific article recommendation still remains one of the challenging issues in education, including learning process. Difficulties in finding related articles from research history and research interest have been experienced by students in collage affecting the duration of study and research time. This paper proposed a new solution by building a search engine to collect and to recommend articles related to student research topics. The system combined the web scraping method as an article data retrieval technique on google scholar and item-based collaborative filtering to recommend the article.  Parameters result produced based on items of user’s history, including item-searched, clicked, and downloaded. The system was built on a web-based scientific article recommendation system using python programming language. This system recommends articles based on the preferences of users and other users who are affiliated and who have an interest in the same item. This research showed that the validation result from the system obtained a recommendation accuracy value over 0.516801. The percentage of the RMSE error value of the recommendation system is 8.62%, or in other words that the accuracy of the recommendation system is 91.28%

    Citation Recommendation: Approaches and Datasets

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    Citation recommendation describes the task of recommending citations for a given text. Due to the overload of published scientific works in recent years on the one hand, and the need to cite the most appropriate publications when writing scientific texts on the other hand, citation recommendation has emerged as an important research topic. In recent years, several approaches and evaluation data sets have been presented. However, to the best of our knowledge, no literature survey has been conducted explicitly on citation recommendation. In this article, we give a thorough introduction into automatic citation recommendation research. We then present an overview of the approaches and data sets for citation recommendation and identify differences and commonalities using various dimensions. Last but not least, we shed light on the evaluation methods, and outline general challenges in the evaluation and how to meet them. We restrict ourselves to citation recommendation for scientific publications, as this document type has been studied the most in this area. However, many of the observations and discussions included in this survey are also applicable to other types of text, such as news articles and encyclopedic articles.Comment: to be published in the International Journal on Digital Librarie

    Low-rank and sparse matrix factorization for scientific paper recommendation in heterogeneous network

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    © 2013 IEEE. With the rapid growth of scientific publications, it is hard for researchers to acquire appropriate papers that meet their expectations. Recommendation system for scientific articles is an essential technology to overcome this problem. In this paper, we propose a novel low-rank and sparse matrix factorization-based paper recommendation (LSMFPRec) method for authors. The proposed method seamlessly combines low-rank and sparse matrix factorization method with fine-grained paper and author affinity matrixes that are extracted from heterogeneous scientific network. Thus, it can effectively alleviate the sparsity and cold start problems that exist in traditional matrix factorization based collaborative filtering methods. Moreover, LSMFPRec can significantly reduce the error propagated from intermediate outputs. In addition, the proposed method essentially captures the low-rank and sparse characteristics that exist in scientific rating activities; therefore, it can generate more reasonable predicted ratings for influential and uninfluential papers. The effectiveness of the proposed LSMFPRec is demonstrated by the recommendation evaluation conducted on the AAN and CiteULike data sets
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