Aiming at the problems of data sparseness and cold start in social network recommendation systems, a hybrid social network recommendation algorithm based on feature Transform and Probabilistic Matrix Factorization (TPMF) was proposed. Using Probability Matrix Factorization (PMF) method as recommendation framework, trust network, the relationship between the recommended items, user-item score matrix and adaptive weight were combined to balance the impact of individual and social potential characteristics on users. The trust feature transfer was introduced into the recommendation system as valid basis for recommendation. Compared to the User-Based Collaborative Filtering (UBCF), TidalTrust, PMF and SoRec, the experimental results show that the Mean Absolute Error (MAE) of TPMF was decreased by 4.1% to 20.8%, and the Root Mean Square Error (RMSE) of TPMF was decreased by 3.3% to 18.5%. Compared with the above four algorithms, for the cold start problem, the Mean Absolute Error was decreased by 1.6 to 14.7%, and the RMSE was decreased by 1.2% to 9.7%, which verifies TPMF effectively alleviates cold start problem and improves the robustness of the algorithm.

In data mining, mining maximum frequent itemsets instead of mining frequent itemsets can greatly improve the operating efficiency of system. The running time consumption of existing maximum frequent itemset mining algorithms is still very large. In order to solve the problem, a new DiffNodeset Maxmal Frequent Itemset Mining (DNMFIM) algorithm was proposed. Firstly, a new data structure DiffNodeset was adopted to realize the fast calculation of intersection and support degree. Secondly, the connection method with linear time complexity was adopted to reduce the complexity of connecting two DiffNodesets and avoid multiple invalid calculations. Then, the set-enumeration tree was adopted as the search space, and a variety of optimal pruning strategies were used to reduce the search space. Finally, the superset detection technology used in the MAximal Frequent Itemset Algorithm (MAFIA) algorithm was adopted to improve the accuracy of algorithm effectively. The experimental results show that, DNMFIM algorithm outperforms MAFIA and N-list based MAFIA (NB-MAFIA) in terms of time efficiency. The proposed algorithm has a good performance when mining the maximal frequent itemsets in different types of datasets.