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User preference plays a prominent role in many fields, including electronic commerce, social opinion, and Internet search engines. Particularly in recommender systems, it directly influences the accuracy of the recommendation. Though many methods have been presented, most of these have only focused on how to improve the recommendation results. In this paper, we introduce an empirical study of user preferences based on a set of rating data about movies. We develop a simple statistical method to investigate the characteristics of user preferences. We find that the movies have potential characteristics of closure, which results in the formation of numerous cliques with a power-law size distribution. We also find that a user related to a small clique always has similar opinions on the movies in this clique. Then, we suggest a user preference model, which can eliminate the predictions that are considered to be impracticable. Numerical results show that the model can reflect user preference with remarkable accuracy when data elimination is allowed, and random factors in the rating data make prediction error inevitable. In further research, we will investigate many other rating data sets to examine the universality of our findings.

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Text clustering is of great importance in data mining, information fusion, artificial intelligence and some other fields. There are many methods in literatures that can be used to classify text. Most of them require some parameters, such as the number of categories, which should be assigned in advance or estimated in classifying process. However, it is difficult to determine these quantities in text clustering. OPTICS is one of clustering algorithms that does not depend on the preset number of categories. In this paper, we introduce an optimization method based on OPTICS to simplify the cluster identification process and improve classification accuracy, and then apply it on text clustering. We also test it on a widely used text corpus. Results show that the proposed method can weaken the influence of data noise and bring more precision to text clustering.

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