Feature-Based Opinion Mining Approach (FOMA) for Improved Book Recommendation

The text book prescribed to the students at universities helps them a lot in acquiring knowledge and performing well in their courses. However, the recent research suggests there is a decline in the number of text book readers. Since recommender systems help in providing items of users’ need, good and precise recommendation of the books could enhance the users’ affinity toward reading the books. The primary objective of this paper is to present an opinion mining-based recommendation technique which can provide the university students with the promising books for their syllabus. The problem with the existing recommender technique is that these methods take only expert recommendation in consideration and the involvement of the opinion of the users, i.e., students/readers, has not been considered which allow us to understand how the readers perceive the recommended books and whether they are satisfied with the recommendation or not? To address the issue, we consider experts and readers both by employing experts’ recommendation for books at top-ranked universities and exploring users’ reviews on the concerned book at online retailers’ sites such as Amazon. To validate the efficacy of the proposed algorithms, eight different parameters have been used; up to 55% improvement in the result has been obtained through proposed method. It is envisaged that the adopted opinion mining approach can be very useful for the recommendation of products of other domain too.