現今多用來找出個人化分群方法的半監督式分群演算法，通常都會遇到取樣偏見的問題，這篇論文分別從下列三個面向提出解決這個問題的方法:
1. 利用使用者在資料點上給予的標籤資訊當作半監督式分群演算法的額外資訊
2. 設計一個模型可將資料點自原本的特性空間嵌入至標籤空間並同時對資料點作分群
3. 使用一個高維度的張量分享不同使用者間的知識，以提升個別使用者的分群表現

從我們的實驗中，可以證明我們的演算法具有優異的知識分享能力及分群表現。

這篇論文其他的貢獻包含:
1. 設計一個在高維張量上使用的鬆散一般化式
2. 針對我們的模型，設計一個免CP分解的訓練方式

The modern solution to find personalized clustering is Semi-Supervised Clustering. However, Semi-Supervised Clustering usually has to face the problem of sampling bias. Our work aims to solve this problem by:
1. Use the user-given tags data on the data points as the side-information of semi-supervised clustering
2. Design a single model to embed the data points from feature space to tag space and cluster the data points in one phase
3. Use a high-mode tensor to transfer knowledge from user to user, and enhance the individual clustering performance

Our experiments showed the superior performance and ability to transfer knowledge of our algorithm.

Other contributions of this paper include:
1. Define a sparse regularization term on tensor
2. Design a CP-free update rule of our model

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