The authors propose a hypernym relation classification method based on word pattern, which can effectively alleviate the sparsity problem suffered by the traditional path-based method. Furthermore, this paper makes an effective combination of the path-based method and the distributional method via word pattern embedding. To demonstrate the effectiveness of the proposed approach, the authors manually annotated a Chinese hypernym dataset containing 12000 word pairs. The experimental results show that the proposed word pattern embedding approach is effective and can achieve an F1 score of 95.36%.

Existing recommender systems do not take full advantage of personalization. To address this problem, a novel approach is proposed to mine the opinions and preference of users to build a personalized model for each user or item. Experimental results generated from a real data set show that the proposed approach can improve the accuracy of rating prediction.