由於現在的網路發達，人們可以輕鬆取得大量的資訊，卻很難快速的取得他們感興趣的資訊。以旅遊為例，人們往往可以快速的找到大量景點，但是卻很難快速的找到他們感興趣的景點。推薦系統的出現解決了上述問題。 本篇論文提出一套不需要評分機制的景點推薦系統，並且能同時兼具精確度、召回率和多樣性。 提出的推薦系統透過深度學習模型分析用戶的操作行為來判斷用戶的喜好，並且透過協同過濾來增加推薦內容的多樣性，進而推薦他們可能感興趣的景點。此推薦系統主要的特點如下: (1) 由於它是透過分析操作行為來捕捉用戶的喜好，所以它可以建立在沒有評分機制的環境中。 (2)此推薦系統還考慮了與目標用戶相似的用戶的歷史資料，目的是為了讓推薦的結果能更具多樣性。 本論文主要會用文史脈流平台的數據來驗證系統的可行性，因為文史脈流平台並無評分機制，而且有用戶的操作行為紀錄。這篇論文會介紹資料前處理、推薦的深度學習網路以及協同過濾的方法。

With the rapid development of the Internet, people can easily get a lot of information. However, it is difficult to get the information in which one is interested quickly. A typical example is that people can often find a large number of Point Of Interests (POIs) quickly, but it is difficult to quickly find the POIs in which one is interested for touring. The emergence of a recommendation system solves the aforementioned problem. This paper proposes the POI recommendation system, which doesn’t require a scoring mechanism and has great precision, recall and diversity. The proposed recommendation system uses the deep learning model to analyze the user’s operational behaviors, and then judge the user’s preference. In order to increase the diversity of recommended results, it uses the user-based collaborative-like filtering, which considers the other users’ historical data. Finally, it combines the two results, which are from the deep learning model and the user-based collaborative-like filtering, as the final results. The main characteristics of this recommendation system are as follows: (1) it can be built in an environment without a scoring mechanism because it can catch the user’s preferences by analyzing user’s operational behavior. (2) It also considers similar users’ historical data to make the recommended results more diversity. This work uses the data from the Demodulating and Encoding Heritage (DEH) platform, which does not have a scoring mechanism and has records of the users’ operational behaviors, to verify the feasibility of this recommendation system. In this work, the data preprocessing, the recommended deep learning model and the collaborative-like filtering method are presented.

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