A Web Content Recommendation Method Based on Data Provenance Tracing and Forecasting
How to choose an appropriate releasing strategy for site content, and which one caters to user’s habits, have become the main challenges. This article provides a provenance-aware model to design the content of the website. Based on the user’s browsing history data, it constructs timed automaton that can trace the provenance of the data to find what the user may be interested in, and it establishes a Markov chain model to determine the content of the link relationship. Experiments show this model not only meets the dynamic needs of users when they browses the site, but also gives certain options to the administrator of site content. It provides recommending result efficiently and should have a bright application prospect.
KeywordsContent recommendation Data provenance Timed automation Markov chain
This work is supported in part by China Postdoctoral Science Foundation (2012M511227), Jiangsu Province Postdoctoral Science Research Fund (1101073C), National Natural Science Foundation of China (61170035), Natural Science Foundation of Jiangsu (BK2011022, BK2011702).
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