Intelligent Knowledge Recommendation System Based on Web Log and Cache Data

Abstract

Personalized recommendation service based on the web is the current research focus of intelligent information retrieval and distant education. In this paper, an intelligent recommendation system is proposed. The system depends on the mining results of Web log and cache data, comprehensively evaluates the influence of navigation times, browsing period, and page volume. Through effective classification of the interested pages from Web log and cache data, the interest model of different user groups is established. Meanwhile, a method for recommending the valuable knowledge is put forward, which combines the approach of content-based filtering and collaborative filtering. Experiments demonstrate that the proposed recommendation method is feasible and effective.