Usage of tagging for research paper recommendation

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...

show abstract

“…21 http://www.citeulike.org/faq/data.adp. 22 https://www.kde.cs.uni-kassel.de/bibsonomy/dumps/. 23 http://csxstatic.ist.psu.edu/about/data.…”

Section: Datasets and Architecturesmentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Kazemi et alcompared two well-known content-based recommendation methods-TD-IDF and word-embedding-to compare the efficiency and usability of the two methods in abstract extraction and recommendation system extraction feature generation (Kazemi & Abhari, 2017). Choochaiwattana proposed an academic paper recommendation method based on user's paper annotation (Choochaiwattana, 2010). The experimental results show that the paper recommendation method based on user papers, authors etc.…”

Section: Content-based Recommendationmentioning

confidence: 99%

A Review on Personalized Academic Paper Recommendation

Li¹,

Zou²

2019

CIS

View full text Add to dashboard Cite

With the advent of the era of big data, it has become extremely easy for scientific users to have to access academic papers, which has enhanced their efficiency and capacity to search or browse papers. However, it also faces some problems such as the explosion of the literature or information overwhelming. Many researchers focus on academic paper recommendation service, hoping to help scientific users to find documents more efficiently and recommend interested or potentially interested papers which could assist academic users doing research. Through literature review, this paper make a comprehensive summary of the research on personalized academic papers recommendation, presenting the state-of-art of academic paper recommendation methodologies, pointing out its pros and cons and indicating primary evaluation metrics and popular datasets. Finnaly, we outlook the research trend of personalized academic paper recommendation as a reference for interested researchers.

show abstract

“…Although there have been many studies on the tagging recommendation system, the number of studies addressing academic publications is relatively small. One of the prominent studies on the tagging academic article recommendation method was proposed by Choochaiwattana [1] and the other is Bahulikar's [2] work. Choochaiwattana has created an index of publications by tagging academic publications, and has recommended making a recommendation system using these tags.…”

Section: Related Workmentioning

confidence: 99%

User Profile Based Paper Recommendation System

Kaya

2018

ijisae

View full text Add to dashboard Cite

With the spread of science and the increasing number of researchers working in academic fields, there has also been a significant increase in the number of academic publications. Researchers always follow new works published for keeping their knowledge up to date. However, due to thousands of academic publications published every day from many academic sources, academics are not always able to find publications about their subjects. Today, almost all of online academic databases employ a recommendation module that only considers the studies similar to the paper that the user looked at. However, a recommendation system based on the information of a single article is often not enough. In this study, the proposed method recommends by considering user's publications, user's co-authors and co-authors' papers. Therefore, metadata of the articles published by the researcher in the past are evaluated as time-awareness by the method we proposed. In this way, the most relevant articles to the user's profile can be found by using the proposed method in the data repository created from the exact contents of hundreds of thousands of academic works. The method uses TF-IDF frequency-based similarity analysis method. In the evaluation phase, the performance of the proposed method was examined. The accuracy of the method was measured by several different tests. The results are very promising and demonstrate that the method can produce accurate and quality results.

show abstract

Usage of tagging for research paper recommendation

Cited by 16 publications

References 19 publications

Research-paper recommender systems: a literature survey

Research-paper recommender systems: a literature survey

A Review on Personalized Academic Paper Recommendation

User Profile Based Paper Recommendation System

Contact Info

Product

Resources

About