We give a concise tutorial on knowledge discovery with linear mixed model in movie recommendation. The versatility of mixed effects model is well explained. Commonly used methods for parameter estimation, confidence interval estimate and evaluation criteria for model selection are briefly reviewed. Mixed effects models produce sound inference based on a series of rigorous analysis. In particular, we analyze millions of movie rating data with LME4 R package and find solid evidences for a general social behavior: the young tend to be more censorious than senior people when evaluating the same object. Such a social behavior phenomenon can be used in recommender systems and business data analysis.

This paper presents a method to extract change information from temporal mammogram pairs and to incorporate the temporal change information in the malignant mass classification. In this method, a temporal mammogram registration framework which is based on spatial relations between regions of interest and graph matching was used to create correspondences between regions of current mammogram and regions of previous mammogram. 18 image features were then used to capture the differences (temporal changes) between the matched regions. To assess the contribution of temporal change information to the mass detection, 5 methods were designed to combine mass classification on image features measured on single regions and mass classification on temporal features to improve overall mass classification. The method was tested on 95 pairs of temporal mammograms using k-fold cross validation procedure. The experimental results showed that, when combining two classification results using linear combination or by taking minimum value, the Az score of overall classification performance increased from 0.8843 to 0.8989 and 0.8863 respectively. The results demonstrated that registering temporal mammograms, measuring temporal changes from matched regions and incorporating the change information in the mass classification improves the overall mass detection.