Personalized news recommendation based on consumers' click behavior

摘要

The news browsing sequence of a consumer can be obtained from the consumer's click behavior on the Internet. Here, some potential associations between news using the news browsing sequence of a consumer will be found. Then, personalized news recommendation for different consumers can be provided according to these potential associations. In this paper, an improved personalized news recommendation algorithm based on consumers' click behavior is proposed. Through doing experiments on real news browsing data, the recommendation result is better and the new algorithm is proved to be feasible.