User-generated trajectories (UGTs), such as travel records from bus companies, capture rich information of human mobility in the offline world. However, some interesting applications of these raw footprints have not been exploited well due to the lack of textual information to infer the subject’s personal interests. Although there is rich semantic information contained in the spatial- and temporal-aware user-generated contents (STUGC) published in the online world, such as Twitter, less effort has been made to utilize this information to facilitate the interest discovery process. In this article, we design an effective probabilistic framework named CO ² to <underline>c</underline>onnect the <underline>o</underline>ffline world with the <underline>o</underline>nline world in order to discover users’ interests directly from their raw footprints in UGT. CO ² first infers trip intentions by utilizing the semantic information in STUGC and then discovers user interests by aggregating the intentions. To evaluate the effectiveness of CO ² , we use two large-scale real-world datasets as a case study and further conduct a questionnaire survey to show the superior performance of CO ² .