User Interest Model And Personalized Search For Forest Products Trading Information

The rapid growth of the Internet brings information explosion to our lives, and also brings the "information overload" and "information disorientation" problems. The Web personalized services are aimed at solving these problems. By building an interest model for each user, personalized service technologies will provide intelligent, personalized services based on their characteristics and interests, such as personalized recommendation services, personalized search engine and personalized websites. Nowadays personalized service technologies have gradually came into practical applications from the academic researches. A lot of companies and webs have proposed their own personalized service systems, which became the main strategies to improve user experiences and service quality. The construction and improvement of the forest products trading information websites are key steps for the accomplishment of the forestry informatization of China, and the development of the personalized services of forest products trading information websites is the key technology for the realization of the intelligent forest products trading information websites.In this work, firstly based on the features of the forest products trading information, a compound keywords vector space model was utilized to represent the user interest model of the forest products trading information website. Every compound keyword in the vector space consists of three keywords, which are supply and demand classification, the name of the forest product and the production place, and some information about the specifications, price and the company name of the forest products. Every compound keyword has a user interest value. The learning and updating of the user interest model were realized through the browsing, registering, releasing, copying, bookmarking and commenting behaviors of the users and introducing the forgetting factors for the interest values, respectively. The persistent interests and the temporary interests of the users were distinguished by introducing a temporary interests group. Utilizing this compound keywords vector space model, we then proposed a personalized search engine based on a two-level inverted index. Finally we provided the realization system for the personalized search engine and the analysis of the experiment results.