An algorithm for generating a recommended rule set based on learner's Browse Interest

Abstract

To personalize the recommended learning information according to the interests of the learner, a recommendation rule set generation algorithm based on learner browsing interests was proposed. First, the learner's browsing behavior was captured. A multivariate regression method was used to calculate the quantitative relationship between the learner's browsing behavior and the degree of interest in the web page to generate a learner's current interest view (CIV). With this current interest view, a content-based collaborative filtering personalized information recommendation service was provided to learners. Then, a new weighted association rule algorithm was used to discover the associations between the items, so that the degree of recommendation was obtained. Furthermore, the degree of recommendation was used as a personalized recommendation service for learners with long-term interests. The results showed that the proposed algorithm effectively improved the quality of information recommendation and the real-time performance of the recommendation. Therefore, this algorithm has a good application value in the field of personalized learning recommendation.