IJMTES – Paper Presented in: ‘2 day State Level workshop on Cyber Fest 17’, conducted by: ‘Department of Computer Engineering, Marathwada Mitra Mandal College of Engineering, Pune’ on 22-23 Feb 2017

Journal Title : International Journal of Modern Trends in Engineering and Science


Author’s Name : Chaitrali G Kulkarni | Sanyogita S Kumthekar | Piyusha D Pardeshi | Yashomati G Panchade | Swati Shekapureunnamed

Paper Presented in : ‘2 day State Level workshop on Cyber Fest 17’, conducted by: ‘Department of Computer Engineering, Marathwada Mitra Mandal College of Engineering, Pune’ on 22-23 Feb 2017

Volume 04 Issue 05 2017

ISSN no:  2348-3121

Page no: 43-48

Abstract – In recent years, we have witnessed enumerous reviews from different review websites. These review websites gives a great opportunity to share our viewpoints for various products we purchase. Due to loads of information, we face problem in mining valuable information from reviews to understand a user’s preferences and make an accurate recommendation. Traditional recommender systems (RS) consider some factors, such as product category, user’s purchase records and geographic location. In this work, we propose a sentiment- based rating prediction method (RPS) to improve prediction accuracy in recommender systems along with Dynamic question list for review generation. We use a social user sentimental measurement approach to calculate user’s sentiment on items/products along with that we also consider user’s interpersonal sentimental influence and product reputation, which can be inferred from user’s reviews. At last, we combine three factors-user sentiment similarity, item reputation similarity and interpersonal sentimental influence into our recommender system to make an accurate rating prediction. We also generate Dynamic question list for easy review generation.

Keywords— Item reputation, Reviews, Rating prediction, Recommender system, Sentiment influence, User sentiment, HDFS (Hadoop Distributed File System), HIVE,RPC(Remote Procedure Calls),ACLs(Access Control Lists)


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