隨著通訊技術的發展，基於位置的廣泛應用與研究已經引起許多學者的關注。其中一個熱門的研究議題是適地性搜尋。近幾年來，雖然不少研究專注於搜尋使用者附近的店家，例如：餐廳、飯店和賣場，儘管如此，大多數的研究並沒有考量使用者的喜好。因此，本研究提出一個新的資料探勘技術，名為Preference-Oriented Location-based Search (POLS)。POLS基於使用者的位置，有效地搜尋其附近k個使用者所喜歡的店家。在POLS中，我們提出兩個新的機制來自動學習使用者的喜好。此外，我們也提出量化喜好權重的機制和基於使用者的位置、時間、喜好以及附近店家的屬性來排序店家的機制。根據我們的了解，本論文為第一個同時將適地性搜尋與時間和使用者喜好學習做結合的研究。經由實驗結果顯示，我們所提出的方法在適地性搜尋上有優異的成果。

With the developing telecommunication technologies, a number of studies have been done on the issues of location-based search (LBS) due to wide applications. Among them, one active topic area is the location-based search. Most of previous studies focused on the search of nearby stores, such as restaurants, hotels, or shopping mall. However, such results may not satisfy the users well for their preferences. In this thesis, we propose a novel data mining-based approach, named Preference-Oriented Location-based Search (POLS), to efficiently search for k nearby stores which are most preferred by the user based on the user’s location. In POLS, we propose two preference learning algorithms to automatically learn user’s preference. In addition, we propose a preference weighting algorithm and ranking algorithm to weight the preference and rank the nearby stores based on user location, query time, user preference and store contents. To the best of our knowledge, this is the first work on taking location-based search with temporal and user preference learning into account simultaneously. Through experimental evaluations, the proposed methods are shown to deliver excellent performance.

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