scholarly journals How to Recommend Online Shopping Consumers the Best of Many Sellers? : Online Seller Recommendation System Using DEA Method

2011 ◽  
Vol 16 (3) ◽  
pp. 191-209
Author(s):  
Jung-Nam An ◽  
Sang-Kyu Rho ◽  
Byung-Joon Yoo
Keyword(s):  
Recommendation System ◽  
Online Shopping ◽  
Dea Method

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.

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.

The Relationship between Comments and Recommendation System and Online Shopper Buying Behaviour

2011 ◽  
Vol 204-210 ◽  
pp. 197-200
Author(s):  
Hui Chen
Keyword(s):  
Research Design ◽  
Experimental Research ◽  
Recommendation System ◽  
Online Shopping ◽  
Positive Relation ◽  
Shopping Experience ◽  
Buying Behaviour ◽  
The Relationship ◽  
Shopping Satisfaction

The author aims at studying the relationship between comments and recommendation system and online shopping behaviours. The author defines online shopping behavious as online shopping experience, online shipping satisfaction, online shopping intention and items chose. With 285 study subjects, the author uses experimental research design to research the relations between those factors. The results show that comments and recommendation and online shopping experience have positive relation. Meanwhile, comments and recommendation and online shopping satisfaction and online shopping intention have positive relation. Online shopping experience and online shopping satisfaction have positive relations with online shopping intention. There is remarkable positive relation between online shopping intention and items chosen.

scholarly journals Enhanced Recommendation System for E-commerce Applications

2019 ◽  
Vol 8 (9) ◽  
pp. 473-478
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Web Applications ◽  
Online Shopping ◽  
User Behavior ◽  
Filtering Algorithm ◽  
Recommendation Algorithms ◽  
Collaborative Filtering Algorithm ◽  
Special Subset ◽  
And Behavior

Recommendation algorithms play a quintessential role in development of E-commerce recommendation system, Where in Collaborative filtering algorithm is a major contributor for most recommendation systems since they are a flavor of KNN algorithm specifically tailored for E-commerce Web Applications, the main advantages of using CF algorithms are they are efficient in capturing collective experiences and behavior of e-commerce customers in real time, But it is noted that , this results in the phenomenon of Mathew effect, Wherein only popular products are listed into the recommendation list and lesser popular items tend to become even more scarce. Hence this results in products which are already familiar to users being discovered redundantly, thus potential discovery of niche and new items in the e-commerce application is compromised. To address this issue , this paper throws light on user behavior on the online shopping platform , accordingly a novel selectivity based collaborative filtering algorithm is proposed with innovator products that can recommend niche items but less popular products to users by introducing the concept of collaborative filtering with consumer influencing capability. Specifically, innovator products are a special subset of products which are less popular/ have received less traction from users but are genuinely of higher quality, therefore, these aforementioned products can be captured in the recommendation list via innovator-recognition table, achieving the balance between popularity and practicability for the user

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