A graph-based taxonomy of citation recommendation models
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A graph‑based taxonomy of citation recommendation models Zafar Ali1 · Guilin Qi1 · Pavlos Kefalas2 · Waheed Ahmad Abro1 · Bahadar Ali1
© Springer Nature B.V. 2020
Abstract Recommender systems have been used since the beginning of the Web to assist users with personalized suggestions related to past preferences for items or products including books, movies, images, research papers and web pages. The availability of millions research articles on various digital libraries makes it difficult for a researcher to find relevant articles to his/er research. During the last years, a lot of research have been conducted through models and algorithms that personalize papers recommendations. With this survey, we explore the state-of-the-art citation recommendation models which we categorize using the following seven criteria: platform used, data factors/features, data representation methods, methodologies and models, recommendation types, problems addressed, and personalization. In addition, we present a novel k-partite graph-based taxonomy that examines the relationships among surveyed algorithms and corresponding k-partite graphs used. Moreover, we present (a) domain’s popular issues, (b) adopted metrics, and (c) commonly used datasets. Finally, we provide some research trends and future directions. Keywords Recommender systems · Research paper recommender systems · Algorithms taxonomy · Information retrieval · Survey · Neural networks
* Zafar Ali [email protected] Guilin Qi [email protected] Pavlos Kefalas [email protected] Waheed Ahmad Abro [email protected] Bahadar Ali [email protected] 1
School of Computer Science and Engineering, Southeast University, Nanjing, China
2
Department of Informatics, Aristotle University, Thessaloniki, Greece
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Z. Ali et al.
1 Introduction The Web contains billions of web pages with information on almost every aspect of daily activities. Moreover, the increasing rate of data on these pages, makes it difficult to support users with relevant information. To cope with these problems, recommender systems have been introduced to facilitate users with personalized suggestions. Additionally, with the rapid development of information technology, the number of research articles on various digital libraries is growing exponentially. Therefore, it is very difficult for a researcher to find research articles related to his/her research needs. To this direction, many models were presented in literature (Meng et al. 2013; Chakraborty et al. 2015; Caragea et al. 2013; Lee et al. 2013; Ebesu and Fang 2017; Alotaibi and Vassileva 2018; Sugiyama and Kan 2013; Bansal et al. 2016) providing personalized citation recommendations. In academic research, such models help researchers to find relevant articles that meet their research interests and information needs. These models use one of the following techniques: (1) Collaborative filtering (CF) (Wang et al. 2014; Bansal et al. 2016; Wang and Li 2015), (2) Content-based (CB) (Amami et al. 2016; Bhagavatula et al.
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