現今網路普及率極高，許多企業都選擇在網路上銷售產品。為了多類型產品的促銷，不少購物網站都會向瀏覽者推薦其他的產品，因而衍生出推薦系統的需求。推薦系統可以採用許多不同的技術來實作，其中集群分析(Clustering)為資料探勘中常被使用的分析方法，其主要原理為藉由資料間的相似特性將資料分群。此外，部分網站也讓消費者能夠給產品評分，藉以收集更多的推薦資訊。但在實務上，大部分的使用者會主動給予評分的狀況非常少，導致相關 K-means 等類似技術在計算時的矩陣稀疏性。為此，本論文比較可兼容分類變數的K-mode分群方法與採用數值資料的K-means分群法，探討兩者應用 Movielens線上影片評價資料製作推薦系統的預測成效。 Nowadays, information technology is well developed and there is lots of information on the Internet, including all kinds of product reviews and user information, which can be used to develop recommender systems. The techniques used in recommender systems include classification prediction, cluster analysis, association rule analysis, etc. Based on MovieLens movie review dataset, we develop and compare movie recommender systems using clustering techniques, with or without the presence of categorical user information. The result shows that our proposed movie recommender system via K-modes clustering method generally performs better than the traditional K-means method when the number of movies in the recommendation list is less than 40.
