Abstract
Academic reading plays an important role in researchers’ daily life. To alleviate the burden of seeking relevant literature from rapidly growing academic repository, different kinds of recommender systems have been introduced in recent years. However, most existing work focused on adopting traditional recommendation techniques, like content-based filtering or collaborative filtering, in the literature recommendation scenario. Little work has yet been done on analyzing the academic reading behaviors to understand the reading patterns and information needs of real-world academic users, which would be a foundation for improving existing recommender systems or designing new ones. In this paper, we aim to tackle this problem by carrying out empirical analysis over large scale academic access data, which can be viewed as a proxy of academic reading behaviors. We conduct global, group-based and sequence-based analysis to address the following questions: (1) Are there any regularities in users’ academic reading behaviors? (2) Will users with different levels of activeness exhibit different information needs? (3) How to correlate one’s future demands with his/her historical behaviors? By answering these questions, we not only unveil useful patterns and strategies for literature recommendation, but also identify some challenging problems for future development.
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Notes
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Note here we treat those IPs with extremely high volume of accessed papers (i.e., more than 3000 a year) as portals rather than real users.
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Acknowledgements
The work was funded by 973 Program of China under Grant No. 2014CB340401, the National Key R&D Program of China under Grant No. 2016QY02D0405, the National Natural Science Foundation of China (NSFC) under Grants No. 61232010, 61472401, 61433014, 61425016, and 61203298, the Key Research Program of the CAS under Grant No. KGZD-EW-T03-2, and the Youth Innovation Promotion Association CAS under Grants No. 20144310 and 2016102.
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Fan, Y., Guo, J., Lan, Y., Xu, J., Cheng, X. (2017). Academic Access Data Analysis for Literature Recommendation. In: Wen, J., Nie, J., Ruan, T., Liu, Y., Qian, T. (eds) Information Retrieval. CCIR 2017. Lecture Notes in Computer Science(), vol 10390. Springer, Cham. https://doi.org/10.1007/978-3-319-68699-8_4
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