Novel Scheme for Movie Recommendation System Using User Similarity and Opinion Mining: A Recent Study

Abstract

The rise of users in a mobile environment has made movie recommender systems an important research issue. This system helps to recommend top-k movies for target user. To help consumers locate the best movies in a more convenient way, a complete aggregate of user preferences, sentiments (emotions), and reviews is necessary to recommend movies. However, we must consider timeliness and accuracy when dealing with the recommendation system. A different recommendation schemes have been presented includes collaborative filtering, content-based recommender system, and hybrid recommender system. We introduce a movie recommendation system based on a new user similarity metric and opinion mining in this work. The basic goal of this work is to determine the types of movie opinions (positive, negative, or neutral) and to provide users with a top-k suggestion list. We extract aspect-based specific ratings from reviews and also recommend reviews to users depends on user similarity and their rating patterns. Finally, the suggested movie recommendation system was validated using multiple evaluation criteria, and the proposed system outperformed conventional systems.