由於網頁資訊藉著分散式架構的網路環境快速擴散，造成網路上的資訊越來越龐大。在這樣的環境下，使用者取得最適當的資訊也不會是件容易的事，因此為了協助人們快速找尋恰當的資訊，資訊過濾技術的引用便快速地發展起來，而推薦系統便是資訊過濾裡頭的一種智慧型網路技術。推薦系統推薦使用者喜歡的物品，去除使用者不喜歡的資訊。本篇論文提出以自我組織特徵映射圖之優化演算法(Self-Organizing Feature Map-based Optimization，簡稱SOMO)為基礎的推薦系統，來預測使用的喜好並給予適當的建議及推薦，並且將此系統應用於電影推薦上。除了將找推薦者的動作變成兩階段分群外，還改良協同式過濾(Collaborative Filtering)推薦系統在“冷啟動”(cold start)時無法找到推薦者的問題。經由兩階段的分群，日後進入系統的使用者及物品可以快速分配到自己所屬的群落。 在本篇論文中，旅遊者推薦系統是推薦系統的另一個應用，而旅遊者推薦系統被包含在進階旅遊者資訊系統(Advanced Travel Information Systems，簡稱ATIS)的領域。為了能有效達成旅遊者希望完成的旅程，進階旅遊者資訊系統希望提供旅遊者旅遊的相關資訊。在本篇推薦系統是屬於進階資訊系統裡的預先規畫的行程(pre-trip)部分，我們首先收集了有關景點、旅程以及使用者對旅程評分的資料庫，再套用上述之推薦系統，便可預測使用者喜好進行旅程推薦，旅程選擇還可以根據景點的經緯度來決定。為了將推薦系統做成能在車內即時使用，考慮了頻寬的限制，我們還對路徑資訊傳輸做了優化的研究。 In a large-scale distributed network environment like internet, information has been increased and changed continuously. Accessing information in such dynamically changing, heterogeneous and world-wide distributed environments puts a big burden on the users. A possible solution to alleviate information overload by identifying which information a user will find worthwhile is the use of recommendation systems. Recommendation Systems are a kind of web intelligence techniques to make daily information filtering for people. In this paper, we propose a SOMO-based recommendation system which is able to learn personal preferences of users and provide tailored suggestions to users. The goal of Advanced Traveler Information Systems (ATIS) is to provide travelers with the information required to efficiently and effectively complete a desired trip. We construct a web site to collect route databases and use the SOMO-based recommendation system to implement a tourist spots recommendation system. Tourist spots that meet the travelers’ preferences will be suggested by the proposed recommendation system. In addition, we also proposed an algorithm to alleviate the heavy load for transmitting the rout maps. Several experiments were conducted to test the performance of the proposed recommendation system.
