The Research Of Information Filtering Questions On The Web

With the rapid development of Internet, the information on it increases in exponent. It has so huge contents and so many sorts that it may be the largest information resources in the world. How to search out ones' most interested contents and filter out those information demands irrelevant to users decides on whether we can utilize the huge information resources. It has been the focus questions of searching information on the Web, and it is also the research goal of our article.In recent years, information filtering technology, for short, IF, appears in theinformation searching field. It is a kind of systematic method, and can be used to extract individuated information from dynamic data flows to meet user demands. Contrasted to traditional information retrieval pattern, IF technology has higher extensibility and it can fit for large scale of users and huge information, and can provide timely and individuated information services for users. IF has intelligence to a certain extent and higher roboticized degree.This paper introduces the main issues of information filtering including the system structure of information filtering, the characteristic and classification of filtration system, the relation between retrieving and filtering, the common model of filtering system, the evaluation index of the system performance and so on. It dwells on the IF questions as a whole and points out the existent problems in current IF systems.Aiming at the existing problems in current IF systems and based on the existent IF technology, this article combines the neural network method and intelligent agent technology in machine learning field to improve on the filtering algorithm and give the individuated filtering system model which have the intelligent characteristic, subjective ability and extensibility based on multi-agents. The main research issues of this article are as follows: the representation, creation and maintenance of the individuated pattern-base, the improvedthe improved filtering-matching algorithm and the intelligent filtering system model based on multi-agents.As for the representation of the individuated pattern-base, we introduces a new classification representation method based on multi-users and multi-topics, so as to make each profile only denote one user's one topic. This method makes it possible to express explicitly the user's interest. As for the creation of the individuated pattern-base, we adopt the Hopfield neural network model, which has the function of ample association and remembrance and may be used to associate with the user's interest to create the initial individuated pattern-base. And as for the maintenance of it, we use the study method based on user's feedback.In the improved filtering-matching algorithm, we put forward a newfiltering-matching algorithm. This method combines the advantages of Boolean model andvector space model and thinks over the matching degree and similarity degree in the wholeof filtering process. The validity of the algorithm has been proved by simulationexperiments.Combining to the characteristics of the intelligent agent, we advance the individuated filtering system model based on multi-agents and give detailed description of the model including the relation between agents, the function, the structure and the key technology to the implementation of the system as well as the implementation process. Making full use of the characteristics of intelligent agent, this model makes up for the shortcomings of the current information filtering system from intelligence, initiative, extensibility and easy-maintenance. It improves on the efficiency and precision of the whole system and can try its best to help us search out the interesting contents on the Web.