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Title

College Recommender system using student’ preferences/voting: A system development with empirical study

Author

Mr. Y. Subba Reddy and Prof. P. Govindarajulu

Vol. 18  No. 1  pp. 87-98

Abstract

Recommender Systems are an ever trending research that can be applied in various domains. The college recommendation systems for undergraduate students are a challenging area that needs to be explored thoroughly. A college recommendation system provides the means to undergraduate students in their college selection process with a good number of suggestions. In this paper an effective weighted clustering process WCLUSTER is implemented using R-tree data structure. Instead of traditional data clustering approaches, an improved approach using top-k queries is applied for clustering the college data, based on students’ preferences/voting. A new technique was proposed for finding similarity measures between objects by using both values of attributes and their corresponding voting / preferences / ratings for attributes. Traditional methods use distance measures for finding similarity between objects. Proposed method uses voting / preferences / ratings for finding similarity between objects by using top-k query ranking of objects. The preferences were obtained through a well structured questionnaire using which the responses from college students were gathered. Based on the sets of responses as preferences the proposed algorithm was executed. To speed up the query execution process a multidimensional indexing structure called R-Tree was used. Pruning techniques were applied for scalability purpose. This paper introduced a recommendation system for college/course selection. The experimental results showed that applying WCLUSTER in this domain is superior to traditional and previous approaches.

Keywords

recommender system, voting, weighted cluster, top-k query, reverse top-k query, multi-dimensional index tree

URL

http://paper.ijcsns.org/07_book/201801/20180111.pdf