由於資訊科技日益發達相對的資料量成指數成長，傳統的推薦系統架構會遇到資料的稀疏性與擴張性問題而導致無法做到迅速與準確的推薦；在此提出一個結合螞蟻分類演算法與推薦系統結合的架構，利用螞蟻分類演算法工具做為資料前處理的核心模組，找出推薦資料庫中使用者屬性與電影屬性間的關係，例如什麼樣的性別、工作、年齡喜歡哪一類的電影，再依據這些找出的規則把符合這些規則的推薦資料篩選出來做為推薦系統推薦時之參考依據，目的在於透過分類篩選出較有參考價值的資料以增加推薦的準確性，和降低的資料計算量以加快線上推薦的速度。
實驗將以一個電影推薦資料庫做為實驗資料來源，其中包含大約一百萬筆推薦資料，可視為一個 m×n的使用者對物品的推薦值矩陣，分別探討以推薦值為4或5為使用者感興趣物品、及推薦值為4或5且依年代分割的推薦值矩陣來做為分類工具的輸入資料，再利用 Data Mining的分類工具 Mining出一些能代表資料庫特徵的分類規則，依據這些規則從資料庫中擷取出符合這些規則的資料來提供推薦系統計算使用者或物品間的相似度並做出推薦；最後再以推薦系統評估工具針對五種不同的協同式過濾演算法對於未分類及分類後的資料進行推薦準確率與效能的比較。

Because of daily advancement of information technology and the exponential growth of data, traditional recommender system architecture can't do efficient and effective recommendation due to data matrix's sparsity and extension. And we propose a system architecture which combines Ant classification algorithm with recommender system and utilize Ant classification algorithm as data pre-processing module to find the relationship between users and movies in the recommendation database. For example, people belong to which gender, occupation, and age will like which genre of movie. System will collect these kinds of data from recommendation database and use these data as the input of recommendation for similarity and prediction computation. The goal of this system is to filter more valuable data for recommender and improve the accuracy and speed of online recommendation.
We will use a movie recommendation database as experiment data which contains about one million of rating records and can be seen as an m by n user-item rating matrix. We will divide the recommendation database into four kinds of data each have various limitation of rating and time value. Then we input these data into Ant classification tool for mining some useful rules and use the new rules to collect classified data from original recommendation database for correlation or similarity computation. Finally we will evaluate the correctness and speed of the new mining data and original one with five different Collaborative Filtering algorithms.

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