International Journal of Science , Engineering and Management (IJSEM)

Monthly Journal for Science , Engineering and Management

Volume3,April 2018,

Topic : Product Recommendation from Textual Reviews Using Hybrid Filtering Approach

Authors:Sunil Jadhav || Ritambhara Rajeshirke

Abstract:The amount of information on the internet grows rapidly and people need some system to find and access appropriate information. Recommender Systems (RS) are currently used in both the research and in the commercial areas. RS are information filtering systems that deal with the problem of Information Overload. The existing recommendation is based on POI (Point of Interest), Geographical location, User Preference learning and algorithms like LDA, OGRPL (Online Graph Regularized User Preference learning) are used for information extraction and also use the Attribute Pruning (AP), Frank-Wolfe algorithm for improving the performance of the system with some limitation like high retraining cost, unable to capture change in preferences, work for specific value of k. The proposed system recommendation is base on sentiment prediction from textual review using the LDA for feature extraction and is originally based on Hybrid Filtering Approach. K numbers of products are recommended to the user. Hence, the proposed system improves the prediction accuracy of the recommendation system.

Keywords: RS, Sentiment prediction, Hybrid Filtering Approach, Conflation algorithm

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DOI: 01.1617/vol5/iss4/pid63215


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Editor-in-Chief

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Dr.Muhammad Ishtiaq,
Mirpur University of
Science & Technology,
Mirpur, Azad Kasmir.

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IMPACT FACTOR: 2.7

ISSN(Online): 2456-1304

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Thomson Reuters ID : q-6288-2016.
ORCiD Research ID : 0000-0001-9540-6799

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Journal Code : IJSEM
Electronic ISSN : 2456-1304
Impact Factor : 2.7
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