Because of the increased number of users in a mobile environment, movie recommender systems have become an important research topic. This system assists in recommending top-k movies to the target user. To make it easier for consumers to find the best movies, a comprehensive aggregate of user preferences, sentiments (emotions), and reviews is required to recommend movies. When dealing with the recommendation system, however, we must consider timeliness and accuracy. Various recommendation schemes have been presented, including collaborative filtering, a content-based recommender system, and others. as well as a hybrid recommender system In this paper, we present a movie recommendation system based on a new user similarity metric and opinion mining. The primary goal of this work is to identify the different types of movie opinions (positive, negative, or neutral) and to provide users with a top-k list of suggestions. We extract aspect-specific ratings from reviews and recommend reviews to users based on user similarity and rating patterns. Finally, the proposed movie recommendation system was validated using a variety of evaluation criteria, and it outperformed conventional systems.
Author(s) Details
Mother Teresa Women’s University, Tamilnadu, India.
Dr. A. Pethalakshmi
Govt. Arts College for Women, Nilakkottai, Dindigul District, Tamilnadu, India.
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