現今的許多網站,都允許使用者為網站中的物件給予標籤,這個過程也被稱為大眾分類法 (Folksonomy).因為使用者可以自由的輸入標籤,所以標籤正是一個容易取得,而且隱含使用者喜好資訊的有用資料來源。
在各個領域中,一個人的用字遣詞,可能隱含了這個人對於某一方面的了解程度或是喜好程度,懂得某個領域中越多字詞或越深的概念的人,對於該領域有興趣的程度可能是越大的.若能將字詞分類,並決定一個字詞在領域中所處的深淺位置,對於個人化推薦系統應該是有幫助的.而本研究在統整標籤的架構上,用階層的資料結構保留資料庫中盡可能多的相異標籤資訊,標籤在其存在的特徵中會有不同的權重值,我們將使用者的標籤使用歷史和先前建出的特徵比對並建立使用者數據(User-profile)。這個設計的用意是,使用同一類標籤的使用者,他們的數據中的同一特徵的權重值是不一樣的。所以我們使用這樣的使用者數據預測其對物件的評分會有更佳的效果。

Tagging system is generally used in most websites. It allows user giving a label to an object with limit or not. In tagging system with no limit, users can label objects with any word they want. It is so-called folksonomy. Therefore, tag is a useful data resource which is easy to acquire and contains preference information of users. Words used by a person in a domain may imply how he knows that domain or how he interested in it. When he knows more key words or difficult concepts, the possibility that he has interested in specific domain is higher. Because a tag has different meaning in multiple situations, so it should have different weights in different cases. If we can group tags and decide the depth of tags in each group, then a tag can has distinct weight values in all groups which it appears. We compare user tagging history with these tag groups to build user-profile. By using these profiles, it is possible to represent preferences of user more concisely and to predict rating of user to specific item with better performance.

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