As one of the most successful approaches to building recommender systems, Collaborative Filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. The most successful approaches to CF are latent factor models, Latent Dirichlet Allocation (LDA) models, which directly profile both users and products, and trust-based collaborative filtering models, which analyze the connections among users. This paper introduces some innovations to both approaches. The factor, topic and trust models can now be smoothly merged, to build a more accurate combined model. Further accuracy improvements are achieved by extending the models to exploit both explicit and implicit feedback by the users. The methods are tested on the Yelp data, and the results are better than those previously published on that dataset.

Abstract:In this paper, we use four classification algorithms like C4.5, naive bayes, logistic regression and K nearest neighbor. It outlines basic principles of classification algorithms and we analysis and summary each algorithm has its advantages, disadvantages through performance comparison on different datasets.

Abstract-Stroke and shape of the Chinese character with different script has changed hugely among different style. To present the correspondence between Chinese characters component-strokes, this paper designs a component-stroke corresponding system for Chinese characters based on sematic knowledge. Firstly, we acquire the correspondence between components by the way of semi-automatic. Then we get the feature points from the intersection of skeleton and contour by skeleton algorithm, finally we finish the Chinese character component-stroke correspondence process. Our system can present a robust and fast component-strokes correspondence results.