A paper recommendation mechanism for the research support system Papits

Watanabe, Shun; Ito, Takayuki; Ozono, Tadachika; Shintani, Toramatsu

doi:10.1109/deec.2005.3

Cited by 19 publications

(11 citation statements)

References 12 publications

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“…However, most approaches that inferred information automatically used all papers that a user authored, downloaded, etc. [62,94,122]. This is not ideal.…”

Section: Neglect Of User Modelingmentioning

confidence: 99%

“…Middleton et al weight papers by the number of days since the user last accessed them [91]. Watanabe et al use a similar approach [122]. Sugiyama and Kan, who use the user's authored papers, weight each paper based on the difference between a paper's publication year, and the year of the most recently authored paper [115].…”

Section: Neglect Of User Modelingmentioning

confidence: 99%

“…Other approaches modeled their users as a graph [102,103,122], as a list of topics that were assigned through machine learning [91], or as an ACM hierarchy [66]. Of those who used the VSM, all but one used the cosine measure to calculate similarities between user models and recommendation candidates.…”

Section: Content-based Filteringmentioning

confidence: 99%

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Research-paper recommender systems: a literature survey

et al. 2015

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In the last 16 years, more than 200 research articles were published about research-paper recommender systems. We reviewed these articles and present some descriptive statistics in this paper, as well as a discussion about the major advancements and shortcomings and an overview of the most common recommendation concepts and approaches. We found that more than half of the recommendation approaches applied content-based filtering (55 %). Collaborative filtering was applied by only 18 % of the reviewed approaches, and graph-based recommendations by 16 %. Other recommendation concepts included stereotyping, item-centric recommendations, and hybrid recommendations. The content-based filtering approaches mainly utilized papers that the users had authored, tagged, browsed, or downloaded. TF-IDF was the most frequently applied weighting scheme. In addition to simple terms, n-grams, topics, and citations were utilized to model users' information needs. Our review revealed some shortcomings of the current research. First, it remains unclear which recommendation concepts and approaches are the most promising. For instance, researchers reported different results on the performance of contentbased and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and Linnaeus University, Kalmar, Sweden sometimes it performed worse. We identified three potential reasons for the ambiguity of the results. (A) Several evaluations had limitations. They were based on strongly pruned datasets, few participants in user studies, or did not use appropriate baselines. (B) Some authors provided little information about their algorithms, which makes it difficult to re-implement the approaches. Consequently, researchers use different implementations of the same recommendations approaches, which might lead to variations in the results. (C) We speculated that minor variations in datasets, algorithms, or user populations inevitably lead to strong variations in the performance of the approaches. Hence, finding the most promising approaches is a challenge. As a second limitation, we noted that many authors neglected to take into account factors other than accuracy, for example overall user satisfaction. In addition, most approaches (81 %) neglected the user-modeling process and did not infer information automatically but let users provide keywords, text snippets, or a single paper as input. Information on runtime was provided for 10 % of the approaches. Finally, few research papers had an impact on research-paper recommender systems in practice. We also identified a lack of authority and long-term research interest in the field: 73 % of the authors published no more than one paper on research-paper recommender systems, and there was little cooperation among different co-author groups. We concluded that several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspect...

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“…However, most approaches that inferred information automatically used all papers that a user authored, downloaded, etc. [62,94,122]. This is not ideal.…”

Section: Neglect Of User Modelingmentioning

confidence: 99%

Section: Neglect Of User Modelingmentioning

confidence: 99%

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Research-paper recommender systems: a literature survey

et al. 2015

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“…Yarowsky et al [16] refined the method by requiring no intervention from reviewers: the reviewers profilesare obtained from their publications, and then matchedusing LIS to the submission's content. Watanabe et al [17] constructed a scale-free network whose vertices are keywords of reviewers' expertise and manuscripts' topics, and the similarity between two keywords is the probability of similarity between the corresponding vertices. The matching degree is the weighted average of similarities between each pair of keywords of submissions and reviewers.…”

Section: A Previous Research On Reviewer Recommendationmentioning

confidence: 99%

A Three-Layer Network Model for Reviewer Recommendation

2013

2013 Sixth International Conference on Business Intelligence and Financial Engineering

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The reviewer recommendation problem has attracted a large number of researchers. And existing research on this problem fails to provide an integrated framework to combining related information, for example, the matching from the research areas of submissions and reviewers, the citation analysis of articles and avoiding conflict of relationship. In this research, we propose a three-layer network model to solve this problem. And we also demonstrate how particle movement algorithm can be used to recommend reviewers.

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“…19 approaches (21%) were not evaluated [14][15][16][17][18][19][20][21][22][23][24][25][26], or were evaluated using system-unique or uncommon and convoluted methods [27][28][29][30][31]93]. In the remaining analysis, these 19 approaches are ignored.…”

Section: Evaluation Methodsmentioning

confidence: 99%

Research paper recommender system evaluation

Beel¹,

Langer²,

Genzmehr³

et al. 2013

Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation

116

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Over 80 approaches for academic literature recommendation exist today. The approaches were introduced and evaluated in more than 170 research articles, as well as patents, presentations and blogs. We reviewed these approaches and found most evaluations to contain major shortcomings. Of the approaches proposed, 21% were not evaluated. Among the evaluated approaches, 19% were not evaluated against a baseline. Of the user studies performed, 60% had 15 or fewer participants or did not report on the number of participants. Information on runtime and coverage was rarely provided. Due to these and several other shortcomings described in this paper, we conclude that it is currently not possible to determine which recommendation approaches for academic literature are the most promising. However, there is little value in the existence of more than 80 approaches if the best performing approaches are unknown.

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A paper recommendation mechanism for the research support system Papits

Cited by 19 publications

References 12 publications

Research-paper recommender systems: a literature survey

Research-paper recommender systems: a literature survey

A Three-Layer Network Model for Reviewer Recommendation

Research paper recommender system evaluation

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