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In recent years, the online shopping and the online advertisement businesses is growing in a vast way. The reason behind this growth is, the peoples are not having sufficient time for go for a shop. Without seeing the quality of the product directly, the people are ready to buy the product by seeing the other user recommendation of the particular product. This leads an interest / the need to develop the researcher an innovative recommendation framework. Based on the opinion prediction rule, the huge size of words and the phrases which are presented in the unstructured data is modified as a numerical values. The sale of the particular product in an online shopping is depends on its description of the quality, the review of the customer. Based on the positive and negative polarity, an Inclusive Similarity-based Clustering (ISC) is proposed to cluster the extracted related keywords from the user reviews. To evaluate the strength, weakness of the product, estimate the respective features, as well as the opinions, the Improved Feature Specific Collaborative Filtering (IFSCF) model for the feature with aspect opinion is proposed. Finally the complete feedback of the product is estimated by propose the Novel Product Feature-based Opinion Score Estimation process. The main challenge in this recommendation system is the fault information estimation of the reviews and the unrelated recommendations of the bestselling or the better quality product. To neglect these issues, an Enhanced Feature Specific Collaborative Filtering Model based on temporal (EFCFM) is proposed in the recommendation system. Hence the developed EFCFM method is investigated by comparing along with the existing methods in terms of subsequent parameters, precision, recall, f-measure, MAE and the RMSE. The outcome shows that the developed EFCFM approach predicts the best product and produce the accurate recommendation to the customers.

Opinion Based Memory Access Algorithms using Collaborative Filtering in Recommender Systems