MRec-CRM: Movie Recommendation based on Collaborative Filtering and Rule Mining Approach
Taushif Anwar, V. Uma · 2019
Recent years have observed an excellent success of recommender system in the field of movies, social network, tourism and e-commerce (such as eBay and Amazon). Recommender system transforms geophysical restrictions and allows business and individuals to form transaction anytime and anywhere. This approach is widely used by e-commerce organizations to exploit product recommender systems in order to improve user experience and enhance sales. In this paper, we are interested in proposing a collaborative filtering based movie recommendation using rule mining to satisfy individual's requirement. This paper also provides a comparative analysis of the several types of similarity measures namely Cosine, Correlation, Euclidean, Jaccard and Manhattan. Rule mining is done on the rating matrix using TopSeq rule for providing perfect recommendation. Recommendation accuracy is measured using precision, recall and F1 score measure. Finally, it was found that correlation measure based rule mining yields better F1 Score.