Searching Similar Books Based on Student’s Preference for Personalized Education

Personalized education aims to give students a personalized learning schedule according to students’ backgrounds and preferences, and the required learning resources for learning are personalized. On-line bookstore allows students to collect learning recourses on-line through Internet, but the problem of information overload plagues students since it is difficult to find the suitable books with the data becoming diverse and massive. Similarity search aims to find the similar objects to a given query, which can be regarded as a promising solution to the problem of information overload. However, the existing similarity search approaches limit the query into only one object, the students cannot express their preferences personally. In this paper, we proposed a personalized similarity search framework, towards finding the similar books based on student’s preference for personalized education. We build the student-book network based on the students’ ratings for books, and use SimRank to measure the similarities between books according to the student-book network. For satisfying student’s personalized query preference, we allow student to express query with multi-books. A personalized similarity measure is proposed for measuring the similarity between query and candidate book by combining the similarities between books. Experiments on Amazon dataset demonstrate that, when the number of input books are not limited into one, the returned rankings are more consistent with students’ query intentions.