| Summary: | 碩士 === 國立中央大學 === 資訊管理學系 === 103 === Based on the previous research, mostly we applied the Local Resources as the basic analysis in Collaborative Filtering, adopting the Rating Matrix for the analysis and prediction of similarities. For example, the efficacy and the correctness of Item-Based is exclusively determined by the quantity of the collected data and the completeness of Rating Matrix. When the quantity is insufficient, it might cause the Sparsity Problem, and the Cold-Start is another inevitable problem caused by the analysis of Local Resources.
We argued for a new perspective that finding an extra database to assist the Item-Based Collaborative Filtering. No matter under which circumstances, the normal one or encountering the arsematrix and new product, we could apply the extra database to calculate the similarity more accurately, combining the predictions of the two different database to increase the accuracy and success of the final prediction.
We utilize the existed huge database, www, as Global Resources. Within the numerous comment and discussion on the Internet, the more frequently compared or discussed between the two products, the higher similarities they have. In the previous study, with the calculation of the Google Similarity, we gained the similarity information between the two products reflected in www, to soften the problem of adopting Local Resources alone.
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