Chinese College Students' Internet Use: A New Method of Behavior Pattern Recognition with Massive Log Analysis

Subjects: Library Science，Information Science >> Library Science submitted time 2023-07-26 Cooperative journals: 《图书情报工作》

Yan Chengxi Wang Jun Wang Ke

Abstract： [Purpose/significance] It is of great significance to analyze and understand users' daily Web behavior patterns, which not only makes progress in the domain of user behavior analyse and information retrieval theoretically, but also has potential social values and practical significance in promoting personalized service and information recommendation for the undergraduate-oriented enterprises.[Method/process] In this paper, a new method for college students' behavior Web pattern recognition based on large-scale log analysis was proposed. It included a semi-supervised learning algorithm "MaxMatching" based on deep learning and text analysis, and a hybrid model combined with two characteristic entropy (Shannon Entropy and Real Entropy).[Result/conclusion] The empirical results showed that this method has the excellent performance in the algorithm and the result interpretation. Also, it can generalize and present all-round Chinese college students' Web behavior pattern in three aspects of network ability, temporality and topicality. The method and conclusion can effectively expand the methods about semantic understanding of queries in information retrieval, and provide some reference and feasible suggestions to undergraduate-oriented enterprises on personalized recommendation service.

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