Venue Recommendation: Submitting Your Paper with Style

Yang, Zaihan; Davison, Brian D.

doi:10.1109/icmla.2012.127

Cited by 57 publications

(52 citation statements)

References 10 publications

(10 reference statements)

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“…Other related research fields include book recommender systems [239], educational recommender systems [240], academic aler6ng services [241], expert search [242], automatic summarization of academic articles [243][244][245], academic news feed recommenders [246,247], academic event recommenders [248], venue recommendations [249], citation recommenders for patents [250], recommenders for academic datasets [251 ], and plagiarism detection. Plagiarism detection, like many research-paper recommenders, uses text and citation analysis to identify similar documents [252][253][254].…”

Section: R Elated Research Fieldsmentioning

confidence: 99%

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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Section: R Elated Research Fieldsmentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

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“…Ranking systems use h-index and impact factor based metrics, which in turn are debatable. Studies on recommending appropriate publication venues to the researcher for their research paper have explored author's network of related coauthors [4] as well as topic and writing-style information [5]. A similar study [3] suggests that the prestige of a venue depends on several factors such as sponsorship by national or international professional organization, reputation of publisher etc.…”

Section: Introductionmentioning

confidence: 98%

ConfAssist

Singh

Chakraborty

Mukherjee

et al. 2015

Proceedings of the 15th ACM/IEEE-CS Joint Conference on Digital Libraries

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Classifying publication venues into top-tier or non top-tier is quite subjective and can be debatable at times. In this paper, we propose ConfAssist, a novel assisting framework for conference categorization that aims to address the limitations in the existing systems and portals for venue classification. We identify various features related to the stability of conferences that might help us separate a top-tier conference from the rest of the lot. While there are many clear cases where expert agreement can be almost immediately achieved as to whether a conference is a top-tier or not, there are equally many cases that can result in a conflict even among the experts. ConfAssist tries to serve as an aid in such cases by increasing the confidence of the experts in their decision. A human judgment survey was conducted with 28 domain experts. The results were quite impressive with 91.6% classification accuracy.

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“…Despite of friends, seniors and lecturer's recommendation, recommender system play a great help to assist in eliminating the massive list of academic event to attend (Hoang et al, 2016, Pham et al, 2012, Yang et al, 2012. Recommendation system main purpose is to discard option that not related to user preference.…”

Section: Introductionmentioning

confidence: 99%

Expert Based Recommendation for Academic Event

Aliska¹,

Shuib²

2018

IJISE

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With the increasing of academic event such as workshop and seminar offered by the organizer, choosing the right academic event to attend can be a confusing and difficult task. Thus, most of the students rely on friends and lecturer's recommendation which considered experience enough in choosing the right academic event. Transforming the idea of getting a recommendation from a person who has wide experience in academic and research fields into a recommendation technique become the purpose of this study. The recommender system with an expert recommendation will help student to find an academic event that match with their need and interest. The architecture of this recommender system is discussed.

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Venue Recommendation: Submitting Your Paper with Style

Cited by 57 publications

References 10 publications

Research-paper recommender systems: a literature survey

Research-paper recommender systems: a literature survey

ConfAssist

Expert Based Recommendation for Academic Event

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