Abstract
MOOCs have attracted a large number of learners with different education background all over the world. Despite its increasing popularity, MOOCs still suffer from the problem of high drop-out rate. One important reason may be due to the difficulty in understanding learning demand and user interests. To helper users find the most suitable courses, personalized course recommendation technology has become a hot research topic in e-learning and data mining community. One of the keys to the success of personalized course recommendation is a good user modeling method. Previous works in course recommendation often focus on developing user modeling methodology which learns latent user interests from historic learning data. Recently, interactive course recommendation has become more and more popular. In this paradigm, recommender systems can directly query user interests through survey tables or questionnaires and thus the learned interests may be more accurate. In this paper, we study the user interest acquisition problem based on the interactive course recommendation framework (ICRF). Under this framework, we systematically discuss different settings on querying user interests. To reduce performance-cost score, we propose the ICRF user interest acquisition algorithm that combines representative sampling and interest propagation algorithm to acquire user interests in a cost-effective way. With extensive experiments on real-world MOOC course enrollment datasets, we empirically demonstrate that our selective acquisition strategy is very effective and it can reduce the performance-cost score by 30.25% compared to the traditional aggressive acquisition strategies.
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Notes
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The ratings in real-world MOOC platforms can have different numerical intervals. To simplify our work, we assume those ratings can be normalized in the interval between zero and one.
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In the rest of this paper, the ICRF user interest acquisition algorithm and the ICRF algorithm will be used interchangeably.
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The oracle refers to a virtual agent that is omniscient to know true user interests.
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The MOOC platform provides free online courses for both on-campus students and off-campus professional employees in China.
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Acknowledgments
We thank the reviewers for their helpful comments. This work is supported by the National Key Research and Development Program of China (2018YFB1004502), the National Natural Science Foundation of China (61702532) and the Key Program of National Natural Science Foundation of China (61532001, 61432020).
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Li, X., Wang, T., Wang, H., Tang, J. (2018). Understanding User Interests Acquisition in Personalized Online Course Recommendation. In: U, L., Xie, H. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 11268. Springer, Cham. https://doi.org/10.1007/978-3-030-01298-4_20
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