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Collaborative filtering algorithm is widely used in the recommendation system of e-commerce website, which is based on the analysis of a large number of user’s historical behavior data, so as to explore the user’s interest and recommend the appropriate products to users. In this paper, we focus on how to design a reliable and highly accurate algorithm for movie recommendation. It is worth noting that...
Electronic commerce includes all business conduct through information and communication technology. Development of infrastructure, telecommunications, mobile technologies, the internet and social media in recent years, made a tremendous growth in business through e-commerce. Now e-commerce is a vital part of the economic development and helps in employment, FDI and GDP growth in the country. More...
Taking advantage of online customer reviews for recommendation system is becoming increasingly important in e-commerce field due to rich implication information of reviews. By analyzing the sentiments and topics through these reviews, a set of sentimental features (SF) can be exploited to represent customer preference. In this work, we firstly construct user-SF matrix instead of traditional user-item...
Recommender system is a tool that provides suggestions to customers. Recommendations are provided for the products that a customer may like in future or that are close to the target customer. On an e-commerce website good recommendation plays an important role for the seller and the buyer. So far researchers have digged out many methodologies for recommendation that may use explicit ratings or implicit...
Collaborative filtering (CF) is commonly used and successful techniques in recommendation systems (RS) but it has showed some problems like sparsity and cold start. Different techniques are employed to overcome the collaborative problems but there is no one single algorithm which can satisfy the personalized needs of each user. This paper presents a new hybrid recommendation approach to improve the...
Today, E-Commerce has become the largest revenue generation industry, letting seller sell everything from a pen to plane to the customers across the globe. Over an E-commerce platform where user and vendor merely interact with each other, the trust is undeniably the most important factor for users to perform transactions online. But at the same time it can't be assessed directly using some pre-defined...
In this paper we deal with the Internet Shopping Optimization Problem. An extended model that includes price sensitive discounts is considered. A set of algorithms to solve the Internet Shopping Optimization Problem with Price Sensitivity Discounts (ISOPwD) is introduced. The algorithms are designed to consider a different solution quality regarding computational time and results close to the optimum...
Logistics service is of great significance in e-commerce operations, the distribution Vehicle Routing Problem (VRP) directly affect enterprises' logistics cost and distribution service quality. When formulating the plan, it's more in line with time actual needs to optimize algorithm from the characteristics of e-commerce than from the algorithm itself. This article designed a model and algorithm in...
Although personalized recommendation technology has been widely used in the Internet, there are still some problems which should be solved, such as data sparseness problem, “cold start” problem. The paper proposes a multi-B2C crossing ranking recommendation algorithm. According to the new user “cold start” problem, the paper proposes different categories of electronic commerce website access multi-B2C...
E-commerce using cloud-based trading platforms has become a popular approach with the growth of global development in recent years. However, the existence of counterfeits on the platform has threatened the benefits of all stakeholders. This paper proposes a novel scheme named Anti-Counterfeit Deterministic Prediction Model (ADPM), which is designed for detecting counterfeits by using Monte Carlo Model...
Offers on e-commerce websites have been mostly a decision made by companies for advertising or clearing stocks. KAAL algorithm was used on sample transaction data to generate frequent itemsets. These frequent itemsets will give an idea of offers to be made on purchase of base items. With advent of internet, the amount of data being generated by business processes is growing exponentially. This paper...
The precise logistics growth prediction can provide important reference for economic growth and consumer groups. According to the traditional logistics growth prediction method, the ordinary time prediction method was used to predict large deviations, and the result was unstable. So an improved logistic growth prediction of B2C e-commerce was proposed based on nonlinear integral. Firstly, the old...
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 recent years, much research has been devoted to the construction of public-private key pairs; on the other hand, few have synthesized the visualization of the producer-consumer problem. Given the current status of efficient archetypes, leading analysts famously desires the emulation of congestion control, which embodies the key principles of hardware and architecture. In our research, we concentrate...
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...
Thanks to their ability to detect fraud, poor quality and ill-intentioned feedbacks and scores in online environments, robust Trust Reputation Systems (TRS) provide actionable information to support relying parties taking the right decision in any electronic transaction. In fact, as security providers in e-services, TRS have to faithfully calculate the most trustworthy score for a targeted product...
Online shoppers purchase a wide variety of items ranging from books to electronics. Keeping in view of the large number of items available for online purchase, there is a need for sophisticated techniques to help users explore the available products. Composite items, which associate a central item with a set of packages formed by satellite items, can be used for this purpose. The existing approach...
Trust has become an important factor to restrict transactions between the network entities in e-commerce. How to judge the relationship of trust and measure the trust between the trading entities has become a serious problem. This article proposes a trust evaluation model based on fuzzy theory for the e-commerce, which solve the uncertainty and subjectivity of e-commerce entity during the processing...
Recommendation systems apply statistical and knowledge discovery techniques to the problem of making product recommendations and they are achieving widespread success in E-Commerce these days. A successful recommendation system fulfils several purposes and the choice of the methodology significantly influences the quality of recommendations and other aspects including scalability. As the volume of...
This paper is based on e-commerce web sites how to use web mining technology for providing security on e-commerce web sites. The connection between web mining, security and e-commerce analyzed based on user behavior on web. Different web mining algorithms and security algorithm are used to provided security on e-commerce web sites. Based on customer behavior different web mining algorithms like page...
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