A comprehensive analysis on movie recommendation system employing collaborative filtering

Collaborative Filtering (CF) is one of the most extensively used technologies for Recommender Systems (RS), it shows an improved intelligent searching mechanism for recommending personalized items. It effectively makes use of the information retained by the application to find similarities between the sections of the application. Apart from RS, other applications of CF making use of the sensing and monitoring of data are environmental sensing, mineral study, financial services, marketing, and many more. Different industries like Tourism, Television, E-Learning, etc. make use of this technology, software such as Customer Relationship Management also make use of this technology. This paper discusses the prowess CF algorithm and its applications for Movie Recommendation System (MRS). It gives a brief overview of collaborative filtering consisting of two major approaches: user-based approach and Item-based approaches. Further, in model-based filtering methodology, it is discussed how machine learning algorithms can be implemented for movie recommendation purposes and also to predict the ratings of the unrated movies and bifurcate or sort movies as per the user preference. Followed by, it throws some light on the methodologies used in the late past and some of the basic approaches that are taken into consideration to incorporate it into MSR. Additionally, this paper anatomized many of the recent past studies in depth to draw out the essence of the researches and studies, its crucial steps, results, future scope and methodologies, followed and suggested by multiple researchers. Finally, we have discussed various challenges in MRS and probable future developments in this field. It is to be noted that various challenges in the field of CF recommendation systems like cold start, data sparsity, scalability issues, etc. were raised and many approaches tried to tackle these challenges in innovative and novel ways. Conclusively CF algorithm is a highly efficacious technique for the application of MRS and its integration with other techniques will lead students, researchers and enthusiasts to more cogent approaches for MRS.