Mining Web Transaction Patterns in Electronic Commerce Environment
With the popularity of the Internet and the development of e-commerce, the recommendation system has gradually become an important component of ecommerce IT technology, and has drawn the more and more attention from esearchers and business people. However, most of the existing e-commerce systems use only part of the information available to make recommendations. With the development of the research, the new e-commerce recommendation system should take advantage of as much information as possible, collect various types of data, efficiently integrate multiple recommend technologies in order to provide more effective recommendation services. At the same time, the expansion ability and the real-time requirement of the recommendation system are becoming more and more difficult to guarantee in a large-scale e-commerce recommendation system. So, the integration of multiple recommendation algorithms using various data and the real-time requirement are pressing problems in the development of e-commerce personalized service. In view of rich sources of e-commerce data, the key to problems can be nothing but the widespread application of the data mining technology and the establishment of a recommendation system model that can operate highly efficiently with multiple recommendation algorithms using various data.
4.2. System Architecture & Design
E-commerce personalized recommendation system and data mining Data mining is an uncommon process to extract the previously unknown and potentially useful information and knowledge from massive, incomplete, distributed, fuzzy and random data. This technology is widely used in classification, prediction and pattern recognition and so on. The biggest advantage of data mining technology for e-commerce is the massive data produced by the ecommerce conducts, which make just basis for data mining. At the same time, the e-commerce user information has many good characteristics such as the rich record, the good data type, research results that are easy to transfer. Therefore, data mining is very much necessary and applicable in the e-commerce recommendation system
The data mining system based on multi-agent
Agent generally refers to calculation entity with characteristics, such as independence, duration, sociality and acting as agent under certain environment. It has its own knowledge bases and reasoning mechanism, which can make a voluntary response to environment. Multi-Agent system (MAS) is a system composed of a lot of Agents, and generally these Agents exchange information through network infrastructure. In order to succeed in communicating, a certain Agent needs to cooperate and consult with other Agent . In MAS, the ability of an individual Agent is limited, but multi-Agent can finish a lot of complicated tasks through cooperation . MAS can improve enterprises capability to mine customer's information effectively, which can economize much time and energy. Its basic thought is that a user corresponds to one Agent. When users search for goods on the e-commerce website, the management Agent will carry on pretreatment for data and establish user Agent, then mine the data and give information feedback to users finally, offer the individualized service. The frame of the system is designed as fig. 4.1 shows.
4.3. System Structure
4.4. The Functions of Agent in System
(1) Management Agent
It receive user's request from the user's graphic interface, then look over whether there is user's information in Agent information storehouse. If it does not exist, establish a user Agent for this user, and provide a systematic serial number, and initialize the user's model storehouse; if it does exist, activate the user Agent. And send users' demand to user Agent, then give the information feedback excavated by mining Agent to the user. With collecting,...
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