Web Community Marketing Research Based On User Characteristic And Interest Mining

With the rapid development of Web communities, Web-based community network marketing recieves more and more attention from business. Survey data shows, by the end of 2010, community users have reached 294 million, accounting for 70.3% of total Internet users, and the national Internet advertising market share reaches 32.12 billion yuan in 2010. However, with the change of purchases in developing society, people tend to search related information on the internet for decision-making, instead of relying on traditional internet advertising. As a product of network marketing and promotion, community marketing plays a very important role in the consumer decision-making by word-of mouth advertising. For consumers, they trust the information among people more than advertisers. Therefore, Web-based community network marketing becomes a low-cost, high efficiency way of information promotion.Because of the short development period, Web community marketing has not yet built an effective theory and a unified approach. As the core of Web community marketing is the interaction and precision marketing, this paper studies four aspects: How to choose the appropriate community for community marketing; How to make users access to appropriate topic in community; how to mine the true characteristics and interests of the virtual user; how to find out-dated topic of community. Based on this, this paper solves some basic technical problems of Web community marketing and builts the basic theory. Main contents are as follows:(1)About how to choose Web community, this paper proposes a Web community ranking theory based on data quality assessment and sampling methods. The establishment of data quality gives a quantitative criterion for the evaluation of Web community data sources, which makes the evaluation criterion be measured and extended. This approach solves the problem that criteria in traditional sort algorithms can not completely reflect the real evaluation; and through the appropriate sampling method, which randomly draws out samples from large community topics so that samples can reflect the overall characteristics of the community, solves the problem about bad metrics of huge number of topics.(2) According to the fuzzy search of community resources, this paper proposes a new fuzzy algorithm based on Trie tree. When a user only remembers part of a word, the user just need to enter the remembered part, our system can still find the desired results. What's more, our system has interactive characteristics:when a user enters a letter, the system will prompt the user possible target word in time. Experiments show that the algorithm can efficiently implement the system.(3)In view of users' characteristics and interests mining, this paper presents a method for users' characteristics and interests mining based on ontology semantic analysis. Through building users' behavior model and characteristics model, establishing a characteristic set of properties and inferred properties of rule sets, and then creating uncertainty inference method, to infer the user's characteristics and interests according to the user's behavior characteristics and attributes of speech. Experimental results show that the method has good scalability and accuracy and solves the problem on the precise location of targets in Web communities marketing.(4) In order to improve the efficiency of mining user characteristics attributes and interests, this paper puts forward a mining method of user characteristics based on the interactive relationship. In this paper, according to a lot of data statistics and analysis, we present an evaluation method based on the theory of hypothesis testing, proving the sociologist's point of view about "intimate friends have more similar interests" also has applicability in the virtual Web community. Afterwards in terms of statistical regularities, this paper constrcts the user group discovery algorithm. Final results show that this is a fast and effective method on mining user groups who have some interest.(5) Aimed at the problem about out-dated topics in Web community, this paper presents the modeling, measurement, reasoning and discovering methods of time consistency of topic pages in Web community. Time Consistency of Web pages which related to the timeliness and content accuracy is that the time webpages referred to matches the actual time, it is an important indicator for evaluating the quality of network information. Many time-sensitive pages exist time inconsistency, seriously affecting the user's understanding of content and decision-making. This paper firstly constructs a model on the time dimension of the theme pages, including time-sensitive analysis of web information, time series-based classification and time dimension extraction of webpages; then measures and reasons on the web time consistency, including time inconsistency classsification of web events, time inconsistency modeling of web events and time inconsistency discovering of topic pages. This method can achieve automatic filtering time inconsistency topic in Web communities to improve the user's experience.This study provides theoretical and technical support for the Web community marketing, and solves the problem that how to identify and sort from a lot of Web communities, realizes the fuzzy query method of community topics, addresses how to precisely mine users'characteristics and attributes and achieves outdated topic information modeling and discoverying method in Web community.