Special Issue Article Open Access

Ranking Documents in IR Using Vector Based Ordering In E-Learning

Abstract

E-Learning provides enormous collection of e-learning materials to the users. But, Most of the retrieved learning materials may be irrelevant to the query posted by the users. The Users spent lot of time to retrieve the pertinent learning materials in the largest domain. Due to this the learning process of a learner is slowed down. Hence, there is a need to develop an efficient retrieval and ranking method in the information learning system. In classical information retrieval model, various strategies were used to rank the documents. These methods ranked the documents based on the retrieval status value which can be computed by using various aggregation operators. These methods rank order the documents without considering the importance of individual term relevance. This paper presents a technique called vector based possibility framework to enhance the performance of classical information retrieval method. This proposed system provides highly relevant learning materials to the learner and it recommends the items based on individual term relevance with respect to the query specified by the user.

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