Abstract
``Is there any pattern in location-based, mobile check-in activities?'' ``If yes, is it possible to accurately predict the intention of a user's next check-in, given his/her check-in history?'' To answer these questions, we crawl and analyze probably the largest mobile check-in datasets, containing 20 million check-in activities from 0.4 million users. We provide two observations---`` work-n-relax'' and ``diurnal-n-nocturnal''---showing that the intentions of users' check-ins are strongly associated with time. Furthermore, the category of each check-in venue, which reveals users' intentions, has structure and forms taxonomy. In this paper, we propose Nested LSTM that takes both (a) check-in time and (b) taxonomy structure of venues from check-in sequences into consideration, providing accurate predictions on the category of a user's next check-in location. Nested LSTM also projects each category into an embedding space, providing a new representation with stronger semantic meanings. Experimental results are poised to demonstrate the effectiveness of the proposed Nested LSTM: (a) Nested LSTM improves Accuracy@5 by 4.22% on average, and (b) Nested LSTM learns a better taxonomy embedding for clustering categories, which improves Silhouette Coefficient by 1.5X. Both results (a)(b) are compared with LSTM-based, state-of-the-art approaches.