scholarly journals Trust Recommendation System in Online Shopping Using Text Mining

2019 ◽  
Keyword(s):  
Text Mining ◽  
Recommendation System ◽  
Online Shopping

A medical specialty outpatient clinics recommendation system based on text mining

2021 ◽  
Vol 12 (4) ◽  
pp. 450
Author(s):  
Qing Chang Li ◽  
Xiao Qi Ling ◽  
Hsiu Sen Chiang ◽  
Kai Jui Yang
Keyword(s):  
Text Mining ◽  
Recommendation System ◽  
Medical Specialty ◽  
Outpatient Clinics

Detection of Fake Online Reviews Using Semi-Supervised and Supervised Learning

2020 ◽  
Vol 17 (8) ◽  
pp. 3577-3580
Author(s):  
M. S. Roobini ◽  
B. Nikhil Chowdary ◽  
J. Madhav Chowdary ◽  
J. Aruna ◽  
Anitha Ponraj
Keyword(s):  
Text Mining ◽  
Supervised Learning ◽  
Online Shopping ◽  
Online Reviews ◽  
Business Organizations ◽  
Main Aspect ◽  
Web Based ◽  
Control Item

Online reviews have an incredible effect on the present business and trade. The development of web-based business organizations has pulled in numerous buyers since they provide a scope of items on aggressive costs. The main aspect most buyers depends on while doing online shopping is the review of items for closing the choice of object. Basic leadership for the acquisition of online items generally relies upon reviews given by the clients. Henceforth, deft people or gatherings attempt to control item surveys for their advantages. In perspective on the impacts of these phony surveys, various systems to recognize these were proposed in the research. Because of reviews and its nature, this is hard to group these utilizing only one classifier. Henceforth, the present research discusses a classifier for dealing with identifying such phony reviews. The study also presents the text mining techniques both supervised and semi-supervised to identify counterfeit online reviews just as looks at the effectiveness of the two strategies on the datasets with hotel surveys.

scholarly journals DISCOVER POINTS OF INTEREST BASED ON USERS’ INTERNET SEARCHES THROUGH AN ONLINE SHOPPING WEBSITE

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 975-980
Author(s):  
G. Shahriari Mehr ◽  
M. R. Delavar ◽  
C. Claramunt ◽  
B. N. Araabi ◽  
M. R. A. Dehaqani
Keyword(s):  
Spatial Analysis ◽  
Recommendation System ◽  
Online Shopping ◽  
Contextual Information ◽  
Information Source ◽  
Primary Source ◽  
The Internet ◽  
Points Of Interest ◽  
Internet Searches ◽  
The City

Abstract. In recent years, the development of the Internet plays a significant role in human's daily activities. One of the most important effects of the Internet is the change in the process of shopping. The advent of online shopping leads to establish a new channel for customers to obtain information about their desired goods and demands. Although many customers collect information from online channel, they also wish to try and search for their required goods at the stores. Besides, discovering this data leads to a new source for spatial analysis to find the users’ interests. Therefore, we can consider this data as a contextual information source for spatial analysis or primary source for recommending points of interest (POIs). In this research, our aim is to discover a relation among the users' internet searches and the goods at the stores to recommend the best store to the users. Euclidean distance is used to calculate the similarity between users' searches and the available goods at the stores. The proposed method has been implemented in the city of Tehran, capital of Iran. The results show that the users’ internet search behavior plays an essential role in the recommendation system which provides stores to the users based on the similarity among the users’ internet searches and the available goods at the stores.

VR Supermarket: a Virtual Reality Online Shopping Platform with a Dynamic Recommendation System

2021 ◽  
Author(s):  
Deeksha Shravani ◽  
Prajwal Y R ◽  
Prajwal V Atreyas ◽  
Shobha G
Keyword(s):  
Virtual Reality ◽  
Recommendation System ◽  
Online Shopping

Application of salesman-like recommendation system in 3G mobile phone online shopping decision support

2010 ◽  
Vol 37 (12) ◽  
pp. 8065-8078 ◽  
Author(s):  
Ching-Torng Lin ◽  
Wei-Chiang Hong ◽  
Yi-Fun Chen ◽  
Yucheng Dong
Keyword(s):  
Decision Support ◽  
Mobile Phone ◽  
Recommendation System ◽  
Online Shopping ◽  
3G Mobile Phone

scholarly journals Opinion based Memory Access Algorithms using Collaborative Filtering in Recommender Systems

2019 ◽  
Vol 8 (12) ◽  
pp. 606-612
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Online Shopping ◽  
Prediction Rule ◽  
The People ◽  
Main Challenge ◽  
Opinion Score ◽  
Quality Product ◽  
Online Advertisement ◽  
Feature Based

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.

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