近年來推薦系統已經應用在各行各業，傳統的推薦系統作法大多依賴用戶的歷史資訊或者物品特徵來進行推薦，而與情感分析結合的推薦系統也大多以整體的產品評價作為特徵依據，無法從細部資訊了解用戶對商品的各別喜好程度。
例如一則評論說明：「該手機的解析度很高，但效能很差」，其實體對象為手機，解析度和效能為Aspect，用戶對不同Aspect給出的評價可能不同。因此我們提出latent aspect的概念，將評論句子輸入深度學習模型中，並從句子中提取特徵向量，利用Attention機制將輸入的每個部分賦予不同的重要程度，分配較高的權重在用戶較為關注的Aspect評論描述上，進而提取關鍵的Aspect評論特徵，並結合機率矩陣分解法進行交互訓練，應用於推薦系統。我們使用Amazon review data公開資料集進行實驗，與(Kim et al., 2016)的卷積矩陣分解法方法進行比較，在三個資料集上，預測效果皆提升了2.5%以上。

In recent years, the recommendation system has been applied in all walks of life. Most of the traditional recommendation system practices rely on the user’s historical information or item characteristics to recommend a suitable choice. But the recommendation system combined with sentiment analysis usually uses the overall product evaluation as the feature basis, it is hard to understand the preference of individual products from the detailed information.
We propose the concept of latent aspect, input the comment sentence into the deep learning model, and extract the feature vector from the sentence. We Use the
Attention mechanism to assign each part of the input to different importance levels assign higher weights to the Aspect comment description of users’ attention. Our
goal is to extract key Aspect comment features, combining with a probability matrix decomposition method for interactive training, applied to the recommendation system to achieve better recommendation results.

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