Effective Cross Synthesized Methodology for Movie Recommendation with Emotion Analysis through Ranking Score

Providing accurate movie recommendations to a user with limited computing capability is a challenging task A hybrid system offers a good trade-off between the accuracy and computations needed for such recommendations. Collaborative Filtering and Content-Based Filtering are two of the most widely employed methods of computing such recommendations. In this work, a high-efficient hybrid recommendation algorithm is proposed, which deeds users' contour attributes to screen them into various groups and recommends movie to a user based on rating given by other similar users. Compared to traditional clustering-based CF recommendation schemes, our technique can effectively decrease the time complexity, whereas attaining remarkable recommendation output. This approach mitigates the shortcomings of the individual methods, while maintaining the advantages. This allows the system to be highly reactive to new viewer inputs without sacrificing on the quality of the recommendations themselves. Building on other hybrids of a similar kind, our proposed system aims to reduce the complexity and features needed for calculation while maintaining good accuracy and further enhanced by utilizing Sentiment Analysis to rank the movies and take user reviews into consideration, which traditional hybrids do not take into account. Then analysis was performed on the data set and the results show that the proposed recommendation system outperforms other traditional approaches.