近年來推薦系統對於在學術圈的研究者來講是熱門的主題，有推薦音樂、電影、書本等等各式各樣的推薦系統。在本論文，我們使用hetrec2011-lastfm-2k這個資料集，這個資料集是從社群音樂網站取得來並用來測試我們的音樂推薦系統，最後推薦歌手給使用者。在本論文，我們提出了一個新的方法，結合了典型的協同過濾方法，包含了以使用者為基礎的協同過濾以及以項目為基礎的協同過濾，而我們稱做混合式推薦系統。實驗結果也顯現出新方法的表現結果與典型的方法(以使用者為基礎、以項目為基礎)相比起來較好。此外我們也對資料集做一些分類(由每個使用者對歌手指定的標籤、使用者聽過歌手的次數)並分析其結果且嘗試找出其結果的原因。

Recently, recommender system (RS) is a popular topic in academic arena for researchers. There are recommender systems for music, movie, book and so on. In this paper, we propose a method that combines the classical user-based collaborative filtering method and the item-based collaborative filtering method. We then employ the hetrec2011-lastfm-2k dataset from Last.fm, the social music website, to test the proposed music recommender system. Experiment results show that the proposed method outperforms the classical methods (user-based and item-based). Moreover, we categorize the dataset (e.g., each user provided the tag assignments of artists and each user listened counts of the artists) to analyze the results.

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