International Journal of Trend in Scientific Research and Development


Online-Issn No :
2456-6470
Print-Issn No :
XXXX
Language :
English
Publisher :
R Patel

Indexed - 2019 : IPI Value (4.22)


The Rating Based Recommender System using Textual Reviews A Survey


Article PDF :

Veiw Full Text PDF

Article type :

Original Article

Author :

Mohd. Danish | Meharban Ali

Volume :

3

Issue :

2

Abstract :

Nowadays online shopping is emerging as a growth of business. Customers are getting used to purchasing the items online. Online reviews are an essential resource for users choosing to buy a product, watch a movie or go to a hotel. When it needs to decide the items products through online, the opinions of other users through review matter a lot. It gives a good idea of the product to be purchasable or not. However, people face the information overloading problem. So the problem is as to how to get valuable information from user reviews so as to understand a users preference and make an accurate recommendation. Recommender systems become risen as an essential tool to overwhelm the negative result of information overloading problem. The traditional recommendation system examines some factors like the users buying records, product classification, and users geographic location. This paper is an attempt to discuss the three social factors with some rating prediction algorithms based on user sentiment similarity, item reputation and user circle influence and review the applicable sentiment dictionary to the recommender system. by Mohd. Danish | Meharban Ali "The Rating Based Recommender System using Textual Reviews: A Survey" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21336.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/21336/the-rating-based-recommender-system-using-textual-reviews-a-survey/mohd-danish

Keyword :

User sentiment reviews, Sentiment analysis, Recommender systems, Item reputation, Rating Prediction

Doi :

https://doi.org/10.31142/ijtsrd21336

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