實驗結果證實，我們所提出的推薦系統表現優於先前所提出的模型，尤其是在較少鄰居的案例時，它能有效率地解決稀疏性和可擴展性的問題。
In recent years, cloud model has been successfully integrated into the collaborative filtering (CF) process for addressing the sparsity and the scalability problems. The item-based cloud model CF approach converts item’s ratings data by user into a global item’s characteristics. The user-based cloud model CF approach converts user’s ratings data into a global user’s preference.
In this paper, we present a Unified could-model CF system that integrates the pre-dictions from both the item-based and the user-based cloud model CF systems. This system adopts a significance weighting scheme for fusion the user and item prediction computation.
The experimental results showed that the suggested recommender system outper-formed previous models, especially for cases with small neighbors by effectively solv-ing the sparsity and the scalability problems.
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