Enabling personalized recommendation on the Web based on user interests and behaviors

The dramatic growth of the Web has brought about the rapid accumulation of data and the increasing possibility of information sharing. As the population on the Web grows, the analysis of user interests and behaviors will provide hints on how to improve the quality of service. In this paper, we define user interests and behaviors based on the documents read by the user A method for mining such user interests and behaviors is then presented. In this way, each user is associated with a set of interests and behaviors, which is stored in the user profile. In addition, we define six types of user profiles and a distance measure to classify users into clusters. Finally, three kinds of recommendation services using the clustered results are realized. For performance evaluation, we implement these services on the Web to make experiments on real data/users. The results show that the average acceptance rates of these services range from 71.5% to 94.6%.