[Objective] This study tries to generate personalized contents from cross-domain resources based on the relationship among online tags. [Methods] First, we proposed a cross-domain resource recommendation model. Then, we identified tags appropriate for cross-domain recommendations. Third, we combined the DBSCAN algorithm with the tag vector to obtain the initial recommendation candidates. Finally, we used the TF-IDF algorithm along with the personalized tags to improve the initial list. [Results] The recall, precision, and F-measure of the resource-based recommendation method were 0.82, 0.75, and 0.78. The recall, precision, and F-measure of the user tag based recommendation method were 0.80, 0.74, and 0.77. Our results were strongly correlated to users’ interests. [Limitations] The number of tags for the initial recommendation candidates was small, which could not fully represent the resources. It is difficult to collect tags for the second round recommendation. [Conclusions] Once tags from different domains are related to each other, we can use them to recommend contents from cross-domain resources.
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