Similar to [ 3 ], our task is not to apply a direct relation between paper-citation relations because, in one way or the other, a researcher who is in possession of a research paper directly or indirectly has access to its limited references and also to its citations. Furthermore, the recommendation coverage of most of the current paper recommenders are limited to a certain field of research, this is because recommending papers are stored prior and therefore the system cannot effectively scan the entire databases to find connections between papers. services marketing thesis topics We use C ij to denote citation score between paper i and a cited-paper j from a paper-citation matrix C. Namely, citation context, citation analysis, and content-based. 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.
In this paper, 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. Each attributes of X and Y can be either 0 or 1. how to edit essay successful student These researchers utilized the influence of social properties to suggest relevant information to individual or group of users based on social ties, which can either be strong or weak depending on the tie strength that represents the closeness and interaction frequency between the information source and recipient [ 47 , 48 ]. We then assess the general performance using the three most commonly used evaluation metrics in retrieval systems:
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Given two papers X and Y , each with n binary attributes, the Jaccard coefficient J , is a useful measure of the overlap that X and Y share with their attributes. The Jaccard similarity coefficient J , is given as. This paper presents a collaborative approach for research paper recommender system. Open in a separate window. While [ 3 ], mined the hidden relationship between a target paper and all of its references.
We use C ij to denote citation score between paper i and a cited-paper j from a paper-citation matrix C. Z 10 Represents the total number of attributes where the attribute of X is 1 and the attribute of Y is 0. This approach has been extended in [ 53 ], to include personality behavior in addition to social relations among smart conference attendees. The authors have declared that no competing interests exist.
For each of the citations Cf j , extract all other papers p ri that Cf j referenced other than the target paper p i. Conclusively, the general performance of our proposed approach has outstandingly outperformed the baseline methods based on precision for all values of N. Different researchers proposed the use of a different user provided information such as the use of a list of citations [ 7 ], the list of papers authored by an author [ 8 ], part of paper text [ 2 ], a single paper [ 9 ], and so on.
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The overabundance of information that is available over the internet makes information seeking a difficult task. In doing that, the authors investigated the use of six different algorithms for selecting citations. research papers school students In all the three measures, the Co-citation method performs very low compared to our proposed approach. Some statistics of the utilized dataset is presented in Table 1. On the other hand, our proposed approach performs worse than CCF in a recommendation list of 5 based on recall and F1 performance measures.
Researchers find it difficult to access and keep track of the most relevant and promising research papers of their interest [ 1 ]. In the paper, three approaches were identified for detecting the relationships between papers based on the perspective of paper sources. cheap custom writing air fresheners Mase, "Ontology-based semantic recommendation for context-aware e-learning," in Ubiquitous Intelligence and Computing, ed:
Fig 3 depicts the comparison based on recall. Our approach aimed to deal with scenarios in which: Moreover, F1 measure given by Eq 4 is the harmonic mean between the precision and recall.
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Furthermore, the recommendation coverage of most of the current paper recommenders are limited to a certain field of research, this is because recommending papers are stored prior and therefore the system cannot effectively scan the entire databases to find connections between papers. The major contributions of our proposed approach are as follows; We utilized the advantages of publicly available contextual metadata to propose an independent research paper that does not require a priori user profile. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. To alleviate this problem, they extend their work in [ 8 ], to mine potential citations papers using imputed similarities through the use of collaborative filtering.
The rationale behind the approach is that, if two papers are significantly co-occurring with the same citing paper s , then they should be similar to some extent. A pairwise comparison was then performed to compute the extent of similarities between papers. We utilize the publicly available dataset presented in [ 2 ]. As we have pointed out earlier, all these improvements are largely due to the strictness in qualifying a candidate paper which removed less relevant papers to the target paper.
We then measure and weigh the extent of similarity between the target paper and the qualified candidate papers and recommend the top-N most similar papers based on the assumption that if there exist significant co-occurrence between the target paper and the qualified candidate papers, then there exist some extent of similarities between them. Kan, "Scholarly paper recommendation via user's recent research interests," in Proceedings of the 10th annual joint conference on Digital libraries, , pp. In this paper, 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. Qualify all the candidate papers p c from p ci that has been referenced by at least any of the p ri. As can be observed from Fig 1 , only Rec.