Aiming at the complexity of triple data's inner relation, a co-clustering recommendation model based on the PARAllel FACtorization (PARAFAC) decomposition was proposed. The PARAFAC was used for tensor decomposition to mine the relevant relations and potential topics between the entities of multidimensional data. Firstly, triple tensor data was clustered by using the PARAFAC decomposition algorithm. Secondly, three recommendation models for different schemes were proposed based on collaborative clustering algorithm, and compared for obtaining the optimal recommendation model through the experiment. Finally, the proposed co-clustering recommendation model was compared with Higher Order Singular Value Decomposition (HOSVD) model. Compared to the HOSVD tensor decomposition algorithm, the PARAFAC collaborative clustering algorithm increased the recall rate and precision by 9.8 percentage points and 3.7 percentage points on average on the last.fm data set, and increased the recall rate and precision by 11.6 percentage points and 3.9 percentage points on average on the delicious data set. The experimental results show that the proposed algorithm can effectively dig out tensor potential information and internal relations, and achieve recommendation with high accuracy and high recall rate.

The item recommendation precision of social tagging recommendation system was affected by sparse data matrix. A tensor factorization recommendation algorithm combined with social network and tag information was proposed, in consideration of that Singular Value Decomposition (SVD) had good processing properties to deal with sparse matrix, and that friends' information could reflect personal interests and hobbies. Firstly, Higher-Order Singular Value Decomposition (HOSVD) was used for latent semantic analysis and multi-dimensional reduction. The user-project-tag triple information could be analyzed by HOSVD, to get the relationships among them. Then, by combining the relationship of users and friends with the similarity between friends, the result of tensor factorization was modified and the third-order tensor model was set up to realize the item recommendation. Finally, the experiment was conducted on two real data sets. The experimental results show that the proposed algorithm can improve respectively recall and precision by 2.5% and 4%, compared with the HOSVD method. Therefore, it is further verified that the algorithm combining with the relation of friends can enhance the accuracy of recommendation. What's more, the tensor decomposition model is expanded to realize the user personalized recommendation.

A kind of area matching method that combined weights matrix with similarity coefficient matrix was proposed in this article. The article was organized as follows: first of all, the method got the weights matrix by using color similarity and distance proximity, and the value of the matrix was corrected with an edge matrix for improving correction of the edge pixels. Then a similarity coefficient matrix was adaptively obtained according to each point pairs sum of absolute difference in matching window between left image and right image. Finally, the method was investigated by matching four stereo images (Tsukuba, Venus, Teddy, and Cones) with ground truth provided in Middlebury stereo database and the rate of overall accuracy reaches 91.82%,96.19%,76.6%,86.9%，respectively.