A Review of Personalized Recommendation of Academic Papers
Zhang Xiaojuan1, Liu Yijun1, Liu Jie2, Chen Zhuo2
1.School of Public Administration, Sichuan University, Chengdu 610065 2.School of Computer and Information Science, Southwest University, Chongqing 400715
Abstract Personalized academic paper recommendation (PAPR) aims to provide academic users with a list of papers to meet their personalized needs, which can help academic users surmount the difficulties of accessing papers in our era of big data. This research has been a hot topic in the field of recommendation systems. Thus, this article systematically reviews and analyzes previous studies of PAPR to clarify the development context and current status of this field, illustrate future research directions, and promote the development of such research, as well as summarizing recent research progress on personalized paper recommendation based on papers published in Chinese and international journals and conferences. On this basis, the techniques (i.e., collaborative filtering-based, content-based, and graph-based methods) of personalized academic paper recommendation are first reviewed in detail. Next, the public datasets, evaluation methods, and indicators of personalized academic paper recommendation are summarized and explored. The final results show that there is a lack of comprehensive modeling of researcher interest and research on user privacy protection in existing studies, and there are some shortcomings in research on explainable PAPR, serendipity-oriented PAPR, and evaluation of PAPR. Finally, possible future research directions are pointed out based on the shortcomings of existing research and the overall development trends in the field of recommendation systems.
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