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A novel personalized instructing recommendation system (PIRS) is designed for Web-based learning. This system recognizes different patterns of learning style and Web using habits through testing the learning styles of students and mining their Web browsing logs. Firstly, it processes the sparse data by item-based top-N recommendation algorithm in the course of testing the learning styles. Then it analyzes the habits and the interests of the Web users through mining the frequent sequences in the Web browsing logs by AprioriAll algorithm. Finally, this system completes personalized recommendation of the learning content based on the learning style and the habit of Web usage. Experiment shows that the recommendation model, proposed in this paper, is not only satisfied with the urgent need of the users, but also feasible and effective.