以使用者為基礎的協同過濾(collaborative filtering)演算法是一種被廣泛使用而且有效率的推薦技術，它可以從別人的意見提供給使用者最適合的建議。雖然協同過濾技術已經成功應用在很多地方，但它有著嚴重的資料稀疏性(sparsity)問題。雲模型(cloud model)利用了雲特徵向量來代表整體的使用者的偏好來解決這個問題。以使用者為基礎(user-based)的協同過濾演算法適用在資料密集的時候，而雲模型協同過濾法在資料稀疏時較為穩定。本研究將使用一個混合式的推薦系統來整合以使用者為基礎的協同過濾演算法及雲模型協同過濾演算法的預測結果。實驗結果顯示混合式的推薦系統可以改善稀疏性的問題及改善預測的品質。
User-based Collaborative filtering (CF), one of themost prevailing and efficient recommendation techniques, provides personalized recommendations to users based on the opinions of other users. Although the CF technique has been successfully applied in various applications, it suffers from serious sparsity problems. The cloud-model ap-proach addresses the sparsity problems by constructing the user’s global preference represented by a cloud eigenvector. The user-based CF approach works well with dense datasets while the cloud-model CF approach has a greater performance when the dataset is sparse. In this paper, we present a hybrid approach that integrates the predictions from both the user-based CF and the cloud-model CF approaches. The experimental results show that the proposed hybrid approach can ameliorate the sparsity problem and pro-vide an improved prediction quality.
