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IJSTR >> Volume 8 - Issue 8, August 2019 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Enhanced Feature Specific Collaborative Filtering Model For Aspect Opinion And Temporal Based Product Recommendation

[Full Text]



J. Sangeetha, Dr.V. Sinthu Janita Prakash



User Reviews, Opinion Mining, Collaborative Filtering, Opinion Score, Productís Property, EFCFM, Recommendation Systems.



nowadays, the online purchasing and advertising becomes massively increased due to the increase in utilization of internet services by the users. For the product sale and its quality description, the productís customer review plays a significant role. Thus, the words and phrases with large size in a raw data is converted into numerical values based on the opinion prediction method. The fault prediction of the reviews and inappropriate recommendation of the best product to the users are the main challenging issues in recent days. To avoid these issues, an Enhanced Feature Specific Collaborative Filtering Model (EFCFM) is proposed for Aspect Opinion and Temporal Based Product Recommendation system. Initially, the raw data is preprocessed using stop word removal technique and the keywords from that preprocessed data is extracted using POS tagger which has both positive and negative polarity. The features of the keywords are extracted from the Senti-WordNet database, product property from the POS tagger and the reviews from the user ratings. Then the Enhanced Feature Specific Collaborative Filtering Model is used to calculate the productís strength and weakness. Also it helps to predict the corresponding characteristics and its opinions. After that, the user query is also analyzed and finally, the opinion score based product recommendation is obtained. The proposed EFCFM technique is analyzed comparatively with other existing techniques with the metrics like precision, recall, f-measure, RMSE, and the MAE. The evaluation results show that the proposed EFCFM technique offers best product recommendations accurately to the users.



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