SVM-based learning object ranking with a tilted time window

Nishtha Chopra

Abstract


When improving Mean Average Precision (MAP) in information retrieval and ranking algorithms, there is either no solution that is holistically optimum, or the solution is too computationally costly. Although SVM algorithms are capable of learning from data and generating models that maximize the margin of error, they do so in a competent manner. To determine the relevance of a learning item, it is necessary to examine its value in relation to time. Being adequate is a dynamic idea that varies with usage, demands and growth of a learning object. Learning item rating and retrieval is proposed in this work, which includes the the LO weight is assigned .

Keywords


Learning Objects, Learning Object repository, Support Vector Machine, Ranking, Landmark Model, Tilted Time Window Model.

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