A Novel Rule based Data Mining Approach towards Movie Recommender System

Abstract

<jats:p>The proposed research work is an effort to provide accurate movie recommendations to a group of users with the help of a rule-based content-based group recommender system. The whole approach is categorized into 2 phases. In phase 1, a rule- based approach has been proposed which considers the users’ viewing history to provide the Rule Base for every individual user. In phase 2, a novel group recommendation system has been proposed which considers the ratings of the movies as per the rule base generated in phase 1. Phase 2 also considers the weightage of every individual member of the group to provide the accurate movie recommendation to that particular group of users. The results of experimental setup also establish the fact that the proposed system provides more accurate outcomes in terms of precision and recall over other rule learning algorithms such as C4.5.</jats:p>

Time-Bin-Based Neighbourhood Algorithm for Temporal Effects in Recommendation Systems

“…Collaborative filtering (CF) is a commonly used RS implementation technique in the literature [1][2][3]. From the application perspective, movie-based RS is a favourite research subject in CF [4][5][6]. Since the development of scientific movie datasets, such as MovieLens [7,8] and Netflix Prize [9], more studies are investigating this research topic.…”