在基於位置的手機中是否存在任何模式？如果是，是否有可能準確預測用戶下一次打卡的意圖，基於他/她的打卡歷史？為了回答這些問題，我們抓取並分析可能是最大的手機打卡活動數據集，包含從四十多萬用戶中蒐集來的兩千多萬次打卡活動。我們提供了兩個觀察:「工作與放鬆」和「白天與夜間」，表明用戶打卡的意圖與時間的強烈相關。此外，打卡地點的種類顯示了用戶的意圖，並且有著樹狀圖的結構。在本文中，我們提出嵌套式長短期記憶模型(Nested LSTM)可以從用戶打卡的歷史中同時考慮:(a)打卡時間(b)樹狀圖結構的打卡種類，提供關於用戶的下一個打卡位置的類別的準確預測。嵌套式長短期記憶模型也將每個打卡種類投影到高維度空間中，提供具有更強語義意義的新向量表示。實驗結果準備證明所提出的嵌套式長短期記憶模型的有效性:(a)嵌套式長短期記憶模型在Accuracy@5 中平均進步了4.22%和(b)嵌套式長短期記憶模型為聚類類別學習更好的分類嵌入，可以將輪廓係數(Silhouette Coefficient)提高1.5 倍。結果（a）（b）與基於長短期記憶模型(Long Short-Term Memory)的最先進的方法進行比較。

``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.

1 Introduction 1
2 Problem Definition 7
2.1 Dataset: Foursquare & Jiepang 7
2.2 Motivating Observations 8
2.3 Problem Formulation 11
2.4 Data Preprocessing 12
3 Methodology 18
3.1 Crash Course to Long Short-Term Memory 18
3.2 Nested LSTM 21
4 Experimental Result 25
4.1 Experimental Setup 25
4.2 Models Compared 27
4.3 Result Summary 29
4.4 Taxonomy Embedding Analysis 32
5 Practitioners’ Guide 39
6 Related Work 41
7 Conclusion 43
Reference 44

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