scholarly journals Evaluation of Recommendation System for Sustainable E-Commerce: Accuracy, Diversity and Customer Satisfaction

2020 ◽  
Author(s):  
Qinglong Li ◽  
Ilyoung Choi ◽  
Jaekyeong Kim
Keyword(s):  
Customer Satisfaction ◽  
Deep Neural Networks ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Sustainable Competitive Advantage ◽  
Customer Data ◽  
The Sustainable Development ◽  
Personalized Services ◽  
Recommendation Algorithms ◽  
Positive Effect

With the development of information technology and the popularization of mobile devices, collecting various types of customer data such as purchase history or behavior patterns became possible. As the customer data being accumulated, there is a growing demand for personalized recommendation services that provide customized services to customers. Currently, global e-commerce companies offer personalized recommendation services to gain a sustainable competitive advantage. However, previous research on recommendation systems has consistently raised the issue that the accuracy of recommendation algorithms does not necessarily lead to the satisfaction of recommended service users. It also claims that customers are highly satisfied when the recommendation system recommends diverse items to them. In this study, we want to identify the factors that determine customer satisfaction when using the recommendation system which provides personalized services. To this end, we developed a recommendation system based on Deep Neural Networks (DNN) and measured the accuracy of recommendation service, the diversity of recommended items and customer satisfaction with the recommendation service. The experimental results of is the study showed that both recommendation system accuracy and diversity would have a positive effect on customer satisfaction. These results can further improve customer satisfaction with the recommendation system and promote the sustainable development of e-commerce.

scholarly journals Customer Satisfaction of Recommender System: Examining Accuracy and Diversity in Several Types of Recommendation Approaches

2021 ◽  
Vol 13 (11) ◽  
pp. 6165
Author(s):  
Jae-Kyeong Kim ◽  
Il-Young Choi ◽  
Qinglong Li
Keyword(s):  
Customer Satisfaction ◽  
Recommender Systems ◽  
Recommender System ◽  
Data Sets ◽  
Sustainable Competitive Advantage ◽  
Customer Data ◽  
The Sustainable Development ◽  
Diverse Data ◽  
And Behavior ◽  
The Impact

Information technology and the popularity of mobile devices allow for various types of customer data, such as purchase history and behavior patterns, to be collected. As customer data accumulate, the demand for recommender systems that provide customized services to customers is growing. Global e-commerce companies offer recommender systems to gain a sustainable competitive advantage. Research on recommender systems has consistently suggested that customer satisfaction will be highest when the recommendation algorithm is accurate and recommends a diversity of items. However, few studies have investigated the impact of accuracy and diversity on customer satisfaction. In this research, we seek to identify the factors determining customer satisfaction when using the recommender system. To this end, we develop several recommender systems and measure their ability to deliver accurate and diverse recommendations and their ability to generate customer satisfaction with diverse data sets. The results show that accuracy and diversity positively affect customer satisfaction when applying a deep learning-based recommender system. By contrast, only accuracy positively affects customer satisfaction when applying traditional recommender systems. These results imply that developers or managers of recommender systems need to identify factors that further improve customer satisfaction with the recommender system and promote the sustainable development of e-commerce.

scholarly journals Augmented Context-Based Conceptual User Modeling for Personalized Recommendation System in Online Social Networks

2020 ◽  
Vol 14 (3) ◽  
pp. 1-19
Author(s):  
Ammar Alnahhas ◽  
Bassel Alkhatib
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Recommendation System ◽  
Contextual Information ◽  
Detailed Comparison ◽  
Personalized Recommendation ◽  
User Models ◽  
Proposed Model ◽  
Recommendation Algorithms ◽  
Novel Method

As the data on the online social networks is getting larger, it is important to build personalized recommendation systems that recommend suitable content to users, there has been much research in this field that uses conceptual representations of text to match user models with best content. This article presents a novel method to build a user model that depends on conceptual representation of text by using ConceptNet concepts that exceed the named entities to include the common-sense meaning of words and phrases. The model includes the contextual information of concepts as well, the authors also show a novel method to exploit the semantic relations of the knowledge base to extend user models, the experiment shows that the proposed model and associated recommendation algorithms outperform all previous methods as a detailed comparison shows in this article.

scholarly journals PENGARUH IMPLEMENTASI TOTAL QUALITY MANAGEMENT TERHADAP KEPUASAN PELANGGAN

2019 ◽  
Vol 8 (12) ◽  
pp. 6931
Author(s):  
Arifin Al Amiri M ◽  
Kastawan Mandala
Keyword(s):  
Quality Management ◽  
Customer Satisfaction ◽  
Total Quality Management ◽  
Continuous Improvement ◽  
Operations Management ◽  
Total Quality ◽  
Customer Data ◽  
Variable Focus ◽  
Positive Effect

This research was conducted at Kober Mie Setan. The number of samples taken as many as 150 people Kober Mie customer. Data collection done through questionnaires. Based on the results of the analysis it wasfound that the variable focus on customers, obsession with quality, teamwork and continuous improvement simultaneously had significant effect on customer satisfaction. The results of testing the partial test hypothesis revealed that each variable focused on the customer, obsession with quality, teamwork and continuous improvement had a significant positive effect  customer satisfaction. From the results of the partial test it is known that the focus variable on the customer is the most dominant variable affecting customer satisfaction. The coefficient determination of 0.731 shows that 73.1 percent variations in customer satisfaction can be explained by four independent variables used  the regression equation, while the sisas are 26.9 percent explained other variables outside the four variables used  this study. Keywords: customer satisfaction, operations management, total quality management

The Design and Implementation of E-Commerce Personalized Services Based on Collaborative Filtering Recommendation System

2014 ◽  
Vol 687-691 ◽  
pp. 2039-2042 ◽  
Author(s):  
Meng Han
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Reference Value ◽  
Personalized Recommendation ◽  
Site Management ◽  
Logical Model ◽  
Recommendation Algorithm ◽  
Design And Implementation ◽  
Personalized Services

In this paper, in accordance with the need of e-commerce site management, constructing the logical model of the personalized recommendation system, and use filtering recommendation algorithm to design the personalized recommendation engine. It is necessary to provide certain reference value to improve the personalized recommendation efficiency of e-commerce sites.

A new recommendation system on the basis of consumer initiative decision based on an associative classification approach

2018 ◽  
Vol 118 (1) ◽  
pp. 188-203 ◽  
Author(s):  
Chengxin Yin ◽  
Yan Guo ◽  
Jianguo Yang ◽  
Xiaoting Ren
Keyword(s):  
Experimental Study ◽  
Customer Satisfaction ◽  
Design Methodology ◽  
Recommendation System ◽  
Classification Method ◽  
Personalized Recommendation ◽  
Associative Classification ◽  
Content Type ◽  
Classification Approach ◽  
Potential Buyer

Purpose The purpose of this paper is to improve the customer satisfaction by offering online personalized recommendation system. Design/methodology/approach By employing an innovative associative classification method, this paper is able to predict a customer’s pleasure during the online while-recommending process. Consumers can make an active decision to recommended products. Based on customer’s characteristics, a product will be recommended to the potential buyer if the model predicts that he/she will click to view the product. That is, he/she is satisfied with the recommended product. Finally, the feasibility of the proposed recommendation system is validated through a Taobao shop. Findings The results of the experimental study clearly show that the online personalized recommendation system maximizes the customer’s satisfaction during the online while-recommending process based on an innovative associative classification method on the basis of consumer initiative decision. Originality/value Conventionally, customers are considered as passive recipients of the recommendation system. However, customers are tired of the recommendation system, and they can do nothing sometimes. This paper designs a new recommendation system on the basis of consumer initiative decision. The proposed recommendation system maximizes the customer’s satisfaction during the online while-recommending process.

Recommendation of sustainable economic learning course based on text vector model and support vector machine

2020 ◽  
pp. 1-11
Author(s):  
Xiangfei Ma
Keyword(s):  
Support Vector Machine ◽  
Recommendation System ◽  
Comprehensive Analysis ◽  
Information Space ◽  
Vector Model ◽  
Personalized Recommendation ◽  
Support Vector ◽  
Retrieval Accuracy ◽  
Recommendation Algorithms ◽  
Massive Information

The sustainable economic learning course recommendation can quickly find the knowledge information that the user really needs from the massive information space and realize the personalized recommendation to the user. However, the occurrence of trust attacks seriously affects the normal recommendation function of the recommendation system, resulting in its failure to provide users with reliable and reliable recommendation results. In order to solve the vulnerability of the recommendation system to the support attack, based on text vector model and support vector machine, this paper makes a comprehensive analysis of the current research status of the robust recommendation technology. Moreover, based on the idea of suspicious user metrics, this paper has conducts in-depth research on how to design highly robust recommendation algorithms, and constructs a highly reliable sustainable economic learning course recommendation model. In addition to this, this research tests the performance of the system from two perspectives of course recommendation satisfaction and system retrieval accuracy. The experiment proves that the model constructed in this paper performs well in the recommendation of sustainable economic learning courses.

scholarly journals Toward Mobile Telecommunication Recommendation System through Intelligent Customers Categorization

2014 ◽  
Vol 12 (7) ◽  
pp. 3651-3658
Author(s):  
Rawan Ghnemat ◽  
Edward Jaser
Keyword(s):  
Recommendation System ◽  
Retention Rate ◽  
Fuzzy Rule ◽  
Personalized Recommendation ◽  
Mobile Telecommunication ◽  
Customer Data ◽  
Superior Result ◽  
Business Plans ◽  
Telecommunication Companies ◽  
Competitive Prices

Now a day, usage of mobile devices is becoming indispensable. This is evident with current mobile penetration rates reaching 100% and even more in some countries. Customers across the world are enjoying competitive prices due to high competition among telecommunication companies. As a result of this, it is mandatory for mobile companies to provide high quality services to their customers to retain them. One aspect which will maximize customers’ trust and lead to high retention rate is to offer them a suitable plan that matches their usage.  Mobile customer usage categorization is therefore an essential task to develop intelligent business plans. Personalized recommendation system is needed to dynamically adapt the different customer behaviours with the most appropriate plan for them. In this paper we propose a new automatic approach for costumers’ categorization. This will be the basis for the recommendation system. The proposed method is built using Fuzzy rule and aims at usage behaviour prediction. The rules was extracted from real customer data obtained from a leading provider. Comparison study with other categorization methods has been conducted and showed superior result and demonstrated the potential advantage of the proposed fuzzy based method.

scholarly journals Research on E-Commerce Platform-Based Personalized Recommendation Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Zhijun Zhang ◽  
Gongwen Xu ◽  
Pengfei Zhang
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Prediction Formula ◽  
Recommendation Algorithm ◽  
Real Dataset ◽  
Recommendation Algorithms ◽  
Filling Method ◽  
System Time ◽  
Different Levels

Aiming at data sparsity and timeliness in traditional E-commerce collaborative filtering recommendation algorithms, when constructing user-item rating matrix, this paper utilizes the feature that commodities in E-commerce system belong to different levels to fill in nonrated items by calculating RF/IRF of the commodity’s corresponding level. In the recommendation prediction stage, considering timeliness of the recommendation system, time weighted based recommendation prediction formula is adopted to design a personalized recommendation model by integrating level filling method and rating time. The experimental results on real dataset verify the feasibility and validity of the algorithm and it owns higher predicting accuracy compared with present recommendation algorithms.

To Adapt to Changes in the User Long-Term and Recent Interest of Personalized Recommendation Model

2013 ◽  
Vol 791-793 ◽  
pp. 2143-2146 ◽  
Author(s):  
Hua Yue Chen ◽  
Jing Pu
Keyword(s):  
Recommendation System ◽  
Information Source ◽  
Information Organization ◽  
Personalized Recommendation ◽  
Web Information Retrieval ◽  
Exponential Expansion ◽  
Personalized Services ◽  
Internet Information ◽  
Information Recommendation

With the development and popularization of the information superhighway, people are surrounded by the sea of information. Exponential expansion of Internet information resources, is the vast amounts of information source, its information organization is heterogeneous, diverse, distribution and other features. therefore, can provide users with effective information recommendation, help users to find the valuable information you need the personalized recommendation system won wide attention in the field of Web information retrieval, and also in actual personalization service system has been widely applied in this paper, the personalized services recommendation system architecture to do some research, proposed a distinguishing the user long-term interests and immediate interests provide information to recommend a new model of personalized recommendation.

scholarly journals Exploring Determinants of Consumers’ Platform Usage in “Double Eleven” Shopping Carnival in China: Cognition and Emotion from an Integrated Perspective

2020 ◽  
Vol 12 (7) ◽  
pp. 2790 ◽  
Author(s):  
Xiaoyu Xu ◽  
Luyao Wang ◽  
Kai Zhao
Keyword(s):  
Online Shopping ◽  
Emotional State ◽  
Aggregate Level ◽  
Related Factors ◽  
Research Directions ◽  
The Sustainable Development ◽  
Cognition And Emotion ◽  
Personalized Services ◽  
Key Points ◽  
Positive Effect

There is a great deal of interest concerning how e-commerce in China can be developed in sustainable ways. Answering this question requires not only the strategic management at the aggregate level, but also developing a micro framework that can effectively understand the cognitive-behavioral pathway of consumers in various online contexts. This paper focuses on the “Double Eleven” Global Online Shopping Carnival (GOSC) in China and attempts to investigate the determinants of consumers’ behaviors of shopping platform usage. The distinguishing feature of this study is that we define GOSC as a unique scenario compared to normal online shopping context, where consumers’ emotional state towards such an event plays a larger role in determining behaviors. Based on Cognitive Emotion Theory (CET), the main findings of this paper suggest that (1) consumers’ behaviors of online platform usage can be affected by both cognitions related factors, including price value, gamification and personalized services, and by emotional state such as having arousal and being pleasured; (2) cognition has an effect on emotional state, such as the positive effect of price value on arousal and pleasure or gamification on arousal. Our study, therefore, has highlighted a number of key points to the sustainable development of GOSC. Limitations and further research directions are also discussed.

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