MOVIE REVIEW ANALYSIS AND PREDICTION USING MODIFIED COLLABORATIVE FILTERING AND CLUSTERING WITH REGRESSION

Internet becomes the most popular surfing environment which increases the service oriented data size. As the data size grows, finding and retrieving the most similar data from the large volume of data would become more difficult task. This problem is focused in the various research methods, which attempts to cluster the large volume of data. In the existing research method Clustering-based Collaborative Filtering approach (ClubCF) is introduced whose main goal is to cluster the similar kind of data together, so that retrieval time cost can be reduced considerably. However, existing research methods cannot find the similar reviews accurately which needs to be focused more for efficient and accurate recommendation system. This is ensured in the proposed research method by introducing the novel research technique namely Modified Collaborative Filtering and Clustering with Regression (MoCFCR). In this research method, initially k means algorithm is used to cluster the similar movie reviewer together, so that recommendation process can be done in the easier way. In order to handle the large volume of data this research work adapts the map reduce framework which will divide the entire data into subsets which will assigned on separate nodes with individual key values. After clustering, the clustered outcome is merged together using inverted index procedure in which similarity between movies would be calculated. Here collaborative filtering is applied to remove the movies that are not relevant to input. Finally recommendations of movies are made in the accurate way by using the logistic regression method. The overall evaluation of the proposed research method is done in Hadoop from which it can be proved that the proposed research technique can lead to provide better outcome than the existing research techniques.