傳統上，使用者藉由比對音樂內涵式相似度，檢索其偏好音樂，一般稱之為「內涵式音樂檢索」(Content-based Music Retrieval，CBMR)。然而，現今內涵式音樂檢索方法中，使用者很難於一次檢索中得到較佳的音樂清單。因此，我們結合了三種關聯式回饋方法，分別為「移動查詢點」 (Query Point Movement，QPM)、「搜尋特徵值權重調整」 (Query Reweighting，QR) 和「擴充查詢」 (Query Expansion，QEX)，提出一「以樣式為基礎之內涵式音樂回饋技術」(Pattern-Based Relevance Feedback ，PBRF)。為了解決區域最佳解的問題，我們更進一步提出了自動最佳化搜尋策略，藉由使用者回饋資訊，適當的選擇較佳的搜尋策略。因此，透過整合QPM、QR、QEX及自動最佳化搜尋策略，我們的方法可以從全域搜尋中準確預測出使用者偏好的音樂。實驗結果證實我們的方法在準確率上，優於既有的內涵式音樂檢索方法。

Traditionally, people retrieve preferred music by computing the similarity of music content, namely content-based music retrieval (CBMR). A number of studies on content-based music retrieval have been presented until the present. However, it is not easy to make a high precise search within a query session. It motivates us to develop a query refinement technique called PBRF (Pattern-based Relevance Feedback) that combines three kinds of query refinement techniques, namely QPM (Query Point Movement), QR (Query Reweighting) and QEX (Query Expansion). To deal with the local optimal problem, we additionally propose a novel switch-based search strategy that adaptively selects the best search strategy based on user’s feedbacks. Through the integration of QPM, QR, QEX and switch-based search strategy, the user’s intention can be captured more precisely in the global search space. The experimental results reveal that our proposed approach performs better than existing CBMR methods in terms of precision.

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