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The Research and Application with Web Mining in E-Commerce Recommendation System

Author: XieJing
Tutor: SuYiDan
School: Guangxi University
Course: Computer technology
Keywords: Recommended system Web Mining Clustering E-commerce
CLC: TP391.3
Type: Master's thesis
Year: 2011
Downloads: 47
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Abstract


The 20th century, the network gradually in depth to the millions of households, but also led to the development of new business technology - e-commerce, the system for the user to guide the user to select, convenient to buy needed goods. However, due to an increase in the types of goods, the user is often difficult from the huge amount of catalog accurately identify the commodity itself. Recommendation system is to solve such problems, it can track user behavior, to explore the needs of users, like the mall sales personnel as active introduction of goods for customers, and Discovery to their hobbies recommended, thus contributing to the customer purchase. In the a commodity increasingly competitive environment, commodity recommendation system to help sellers successfully attract customers, reduce churn, promote enterprise sales force and enhance competitiveness. Characteristics of the recommendation system, which has been the concern of many researchers, the development and use of this system will promote large-scale development of the area of ??electronic commerce. The e-commerce system for different user recommendation system knowledge discovery technology, personalized recommendation based on products and services. To meet user demand, enhanced product information on enterprises to strengthen the competitiveness of e-commerce is very feasible. At present, although e-commerce in the commodity recommendation system to obtain a wealth of research results in the many scholars, but still far from being able to meet the growing needs of the e-commerce market. The major challenges faced by the commodity recommendation system focused on e-commerce recommendation system exploration and research in the following three aspects. Elaborate some of the basic concepts and basic knowledge of the Web mining and recommendation system, data cleansing recommendation system workflow studies and data cleaning module design and implementation. 2. Clustering algorithm, most do not have the ability to dynamically adjust with the change of the user's browsing behavior. AiNet clustering algorithm based on incremental clustering algorithm exists for this problem, the idea of ??artificial immune system characteristics and the ant colony incremental clustering algorithm combination is proposed based on the the artificial immune incremental clustering algorithm. At the same time, the proposed algorithm experiments. After experimental verification, based on the the artificial immune incremental clustering algorithm in the average class error and algorithm execution time are reflected in the good performance. 3. Based the artificial immune incremental clustering algorithm based on the design of the e-commerce recommendation system model based on Web mining.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Retrieval machine
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