最近推薦系統成為一個熱門的研究主題，有推薦文章、音樂等等各式各樣的推薦系統，但是絕大多數的推薦系統都只有考慮單一屬性例如使用者的喜好(瀏覽、購買紀錄等等)、推薦項目的內容(文章的內容)。本篇論文題出一個新的方法，將推薦系統結合使用者的背景資訊(職業、年齡……等等)並將資料加以分類來改善推薦的結果。
我們使用MovieLens-1M這個資料集來測試我們的系統，這個資料集包含了6040個使用者與3952部電影，其中使用者的部分又包含了使用者的性別、年齡、職業與對電影的評分，電影的部分則包含了電影名稱與類型。實驗結果證明了推薦系統加入社群資訊以及將資料做分群確實能夠改善推薦的效果。

Recently, recommendation system has become a popular research topic in the study filed. There are recommendation systems for articles, music and so on. But most recommendation systems consider only attributes like user preference (e.g., users’ buying records), content of items (e.g., articles content). This study proposes a new recommendation paradigm that combines users’ background social information (e.g., age, occupations, etc.) using categorize to improve recommendation results.
To evaluate the propose method, we employed the MovieLens-1M dataset that contains 6040 users and 3952 movies. For each user, we have user’s gender, age, occupation and rate of movies. For each movie, we have movie genres and movie titles. Experimental results show that the proposed recommendation paradigm that combines social network information and categorize can improve the recommendation results.

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