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Our proposed approach has recorded significant improvements over the baseline methods in providing relevant and useful recommendations at the top of the recommendation list based on mean average precision and mean reciprocal rank

A collaborative approach for research paper recommender system.

PloS one, no. 10 (2017): e0184516-e0184516

被引用7|浏览15
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摘要

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers' area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several appr...更多

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简介
  • The overabundance of information that is available over the internet makes information seeking a difficult task.
  • The results from this approach largely depend on how good the user is in fine-tuning the query message beside its inability to personalize the searching results.
  • Another classical approach used by most researchers is to follow the list of references from the documents they already possessed [3].
重点内容
  • The overabundance of information that is available over the internet makes information seeking a difficult task
  • Motivated from [3], this paper presents a collaborative approach for research paper recommender system
  • The general performance of our proposed approach has outstandingly outperformed the baseline methods based on precision for all values of N
  • We utilized the publicly available contextual metadata to leverage the advantages of collaborative filtering approach in recommending a set of related papers to a researcher based on paper-citation relations
  • Based on the three most commonly used information retrieval system metrics, our proposed approach have significantly improved the baseline methods based on precision, recall and F1 measures
  • Our proposed approach has recorded significant improvements over the baseline methods in providing relevant and useful recommendations at the top of the recommendation list based on mean average precision (MAP) and mean reciprocal rank (MRR)
方法
  • In assessing the effectiveness of the proposed framework, the authors compare the recommendation results with two baselines presented in [7] and [3].
  • Relation matrix as a rating score and generates the recommendation based on common citations between the target paper and its neighboring papers.
  • The algorithm counts the number of times other citations were co-cited with it.
  • The task was to quantify the degree of closeness between the target paper and the other papers that cited any of the target paper’s references.
  • The rationale behind the approach was that, if two papers are significantly co-occurring with the same citing paper(s), they should be similar to some extent
结果
  • Results and discussions

    To be specific, the results of each evaluation metric represent the overall averages over all the 50 researchers of the dataset.
  • As can be seen from Fig 2, the precision results of the proposed approach has https://doi.org/10.1371/journal.pone.0184516.g003 significantly outperformed the baseline methods (Context-Based Collaborative Filtering (CCF) proposed by [3] and Co-citation method proposed by [7]) in returning relevant research papers for all N recommendations values.
  • This is because the Co-citation method does not infer the hidden associations between paper-citation relations rather applies direct relations between a target paper and its neighboring papers
结论
  • Conclusion and future work

    In this paper, the authors utilized the publicly available contextual metadata to leverage the advantages of collaborative filtering approach in recommending a set of related papers to a researcher based on paper-citation relations.
  • Based on the three most commonly used information retrieval system metrics, the proposed approach have significantly improved the baseline methods based on precision, recall and F1 measures.
  • The authors' proposed approach has recorded significant improvements over the baseline methods in providing relevant and useful recommendations at the top of the recommendation list based on mean average precision (MAP) and mean reciprocal rank (MRR)
总结
  • Introduction:

    The overabundance of information that is available over the internet makes information seeking a difficult task.
  • The results from this approach largely depend on how good the user is in fine-tuning the query message beside its inability to personalize the searching results.
  • Another classical approach used by most researchers is to follow the list of references from the documents they already possessed [3].
  • Objectives:

    The authors' aim is to identify the latent associations that exist between research papers based on the perspective of paper-citation relations.
  • Methods:

    In assessing the effectiveness of the proposed framework, the authors compare the recommendation results with two baselines presented in [7] and [3].
  • Relation matrix as a rating score and generates the recommendation based on common citations between the target paper and its neighboring papers.
  • The algorithm counts the number of times other citations were co-cited with it.
  • The task was to quantify the degree of closeness between the target paper and the other papers that cited any of the target paper’s references.
  • The rationale behind the approach was that, if two papers are significantly co-occurring with the same citing paper(s), they should be similar to some extent
  • Results:

    Results and discussions

    To be specific, the results of each evaluation metric represent the overall averages over all the 50 researchers of the dataset.
  • As can be seen from Fig 2, the precision results of the proposed approach has https://doi.org/10.1371/journal.pone.0184516.g003 significantly outperformed the baseline methods (Context-Based Collaborative Filtering (CCF) proposed by [3] and Co-citation method proposed by [7]) in returning relevant research papers for all N recommendations values.
  • This is because the Co-citation method does not infer the hidden associations between paper-citation relations rather applies direct relations between a target paper and its neighboring papers
  • Conclusion:

    Conclusion and future work

    In this paper, the authors utilized the publicly available contextual metadata to leverage the advantages of collaborative filtering approach in recommending a set of related papers to a researcher based on paper-citation relations.
  • Based on the three most commonly used information retrieval system metrics, the proposed approach have significantly improved the baseline methods based on precision, recall and F1 measures.
  • The authors' proposed approach has recorded significant improvements over the baseline methods in providing relevant and useful recommendations at the top of the recommendation list based on mean average precision (MAP) and mean reciprocal rank (MRR)
表格
  • Table1: Statistics of the utilized dataset
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相关工作
  • Research paper recommenders that provide the best suggestions for all alternatives emerged over the last decade to help researchers on seemingly finding works of their interest over the Cyber Ocean of information. Collaborative filtering (CF) is one of the most successful techniques used in recommender systems [13]. It is a method which recommends items to target users based on what other similar users have previously preferred [14,15,16]. It has been used in various applications such as in recommending movies [17], audio CD [18], e-commerce [19], music [20], Usenet news [16], research papers [7, 21,22,23,24] among others (see [25]). Some researchers [13, 21, 26], have criticized the use of this technique to recommend scholarly papers. Precisely the authors in [21, 26], claimed that collaborative filtering is only effective in a domain where the number of users seeking recommendation is higher than the number of items to be recommended, such domains include movies [27], music [28], news [29] etc. While the argument in [13], is that researchers are not willing to spend their valuable time to provide explicit ratings to their consumed research papers, and therefore, leading to insufficient ratings by the researchers to the research papers. Furthermore, for a user to receive useful recommendations, a tangible number of ratings is required.
基金
  • The authors received no specific funding for this work.
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作者
Khalid Haruna
Khalid Haruna
Damiasih Damiasih
Damiasih Damiasih
Joko Sutopo
Joko Sutopo
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