Extracting the User’s Interests by Using Web Log Data Based on Web Usage Mining

Rao, Kannasani Srinivasa; Krishnamurthy, M.; Kannan, A.

doi:10.1166/jctn.2015.4468

Cited by 2 publications

(1 citation statement)

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“…Xu et al (2009) believed that the behavior of browsing the page contains the attention and interest of the content and it can be used to predict interest, and they pointed out that the user behavior of the relevant interests includes the residence time of browsing the learning page, web link of clicking, and click frequency of a page and so on [38]. Rao et al (2015) extracted user's interests from web log data, including the log of visit time and visit density, and they discussed the technological in data mining and its applications to personalization [28]. Maheswari et al (2015) studied on data preprocessing of web log files and how to predict user's interest, and their research is based on the mining of behavior logs [21].…”

Section: Interest Miningmentioning

confidence: 99%

Construction of affective education in mobile learning: The study based on learner’s interest and emotion recognition

et al. 2017

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Affective education has been the new educational pattern under modern ubiquitous learning environment. Especially in mobile learning, how to effectively construct affective education to optimize and enhance the teaching effectiveness has attracted many scholars attention. This paper presents the framework of affective education based on learner's interest and emotion recognition. Learner's voice, text and behavior log data are firstly preprocessed, then association rules analysis, SO-PMI (Semantic Orientation-Pointwise Mutual Information) and ANN-DL (Artificial Neural Network with Deep Learning) methods are used to learner's interest mining and emotion recognition. The experimental results show that these methods can effectively recognize the emotion of learners in mobile learning and satisfy the requirements of affective education.

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Section: Interest Miningmentioning

confidence: 99%