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E-Commerce products often come with rich and tree-structured content information describing the attributes. To well utilize the content information, this study proposed a fuzzy content matching-based recommendation approach to assist e-Commerce customers to choose their truly interested items. In this paper, users' ratings and preferences are represented using fuzzy numbers to remain uncertainties...
Currently, There are many E-commerce websites around the internet world. These E-commerce websites can be categorized into many types which one of them is C2C (Customer to Customer) websites such as eBay and Amazon. The main objective of C2C websites is an online market place that everyone can buy or sell anything at any time. Since, there are a lot of products in the E-commerce websites and each...
Grounded in users' online purchasing behaviors, we developed a multi-attribute sorting panel for online shopping and incorporated opinion attributes in it. Furthermore, the multi-attribute sorting panel was expanded to three alternative designs that mainly differ in the way of eliciting relative importance for attributes, called direct assessment, indifference method, and indirect measurement. Then,...
During the last two decades we have witnessed the tremendous amount of growth in e-commerce industry. People all over the world buy articles just by a click of mouse. Today recommendation system is an important part of almost every website. A user might not be able to find out all the desired articles and items from the endless information pool available on the internet. Recommender system suggests...
From small local businesses to large multinationals, we can find a lot of initiatives in order to put their business online. E-Commerce is rapidly expanding worldwide with the emergence of new web shops, business to business services, or payment services, just to name a few. E-Commerce companies have been continuously improving their business incomes and extending their influence in the market. However,...
Recommender systems provide users with personalized suggestions about products or services. General task of recommender systems is to improve recommendation accuracy, but this paper mostly focuses on improving the degree of surprise, using SVD++ (singular value decomposition) model. First, logistic regression method is used to process raw data including different sorts of user actions on brands, such...
With the rise of e-commerce business, sales forecasting plays an increasingly important role, for accurate and speedy forecasting can help e-commerce companies solve all the uncertainty associated with demand and supply and reduce inventory cost. As the rapid growth in the amount of data, traditional intelligence models like Neural Networks have weakness in terms of speed. In this paper, we introduce...
Currently, e-commerce is being applied more and more widely in daily life. However, how to give an accurate, real-time and low-cost estimation of online retail sales is still a difficult problem from both academic and industrial aspects. This paper presents an efficient method for the estimation of online retail sales that is characterized by an order detection algorithm embedded in distributed clients...
Nowadays, recommender systems occupy an increasingly important position in people's lives. Recommender systems are widely applied in e-commerce websites, they discover users' potential consuming habits by analyzing their behaviors, and then recommend users with what they may purchase. However, recommender systems on e-commerce sites are facing the problem of data sparsity. Data sparsity may cause...
In recommendation systems, the relationship between information size and recommendation performance is an important research point. Here, we study this relationship based on a new method, variable precision, and design a new algorithm. We demonstrate that recommendation systems perform better with higher data precision, however which should be controlled within a threshold. We collect movie rating...
With the widespread diffusion of social network platforms, e-vendors can now use social network information to provide personalized services to their consumers. Nonetheless, the accuracy of social network-based personalization remains uncertain, as compared to that of traditional personalization approaches. Drawing on social influence and similarity attraction theories, this study compares social...
The present Recommender systems have intrusiveness problem in its operations, provide less accuracy in recommendations and operate on uncertain nature of data. In order to make Recommender systems to provide effortless assistance along with accuracy in recommendations, a combined framework is proposed which combines the implicit relevance feedback, multicriteria ratings and fuzzy linguistic approaches...
Due to the rise and rapid growth of E-Commerce, use of credit cards for online purchases has dramatically increased and it caused an explosion in the credit card fraud. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In real life, fraudulent transactions are scattered with genuine transactions and...
Recommender systems are gaining a great importance with the emergence of E-commerce and business on the internet. These recommender systems help users in making decision by suggesting products and services that satisfy the users' tastes and preferences. Collaborative filtering and content-based recommendation are two fundamental methods used to develop recommender systems. Although, both methods have...
Collaborative filtering is one of the most important technologies in e-commerce recommendation system. Traditional similarity measure methods work poorly when the user rating data are extremely sparse. Aiming at this issue a hybrid collaborative filtering is proposed. This method used a novel similarity measure method to predict the target item rating and it fused the advantages of the user-based...
Based on 287 samples collected from four universities, as well as three virtual communities of online shops in China, the author analyzed the impacts of review system on online shopping intention. A model reflecting characteristics of e-commerce based on TAM was created. Four features of review system (Quality, Quantity, Accuracy, and Interactive) were chose as independent variables, while Usefulness,...
The User-Item missing rating data are a kind of uncertain data in e-commerce website, but in recommendation system these missing ratings are the important information when implementing personalized recommendations. Currently, the existing methods are using a fixed value, the average value of all ratings or a predicted value to replace the missing values. In this paper, to solve the issue which considers...
Recommender System is one of the most important technologies in E-commerce, and the collaborative filtering algorithm is the most widely used technique. In this paper, we proposed an improved collaborative filtering algorithm based on bipartite network, degree of nodes and sort of nodes both have been taken into account. And we only need to calculate the top-N similar neighbors for each target item,...
This paper introduces Web mining technologies, analyzes the Web mining process often used in recommender system. We propose a recommender system model framework and design the recommender system in E-commerce. The model framework consists of data acquisition module, off-line module and online module, which solves the balance problem between recommendation accuracy and respond efficiency. The experiment...
Nowadays, e-commerce is growing fast, so product reviews have grown rapidly on the web. The large number of reviews makes it difficult for manufacturers or businesses to automatically classify them into different semantic orientations (positive, negative, and neutral). Most existing method utilize a list of opinion words for sentiment classification. whereas, this paper propose a fuzzy logic model...
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