For the difference of transmutative Singular Value Decomposition (SVD++) algorithm's rating rule in two stages of model training and prediction, and the same probability of List-wise Matrix Factorization (ListRank-MF) algorithm's Top-1 ranking probability caused by a large number of same rating items, an algorithm of list-wise matrix factorization combining with item popularity was proposed. Firstly, the current item to be rated was removed from the set of items that the user had used in the rating rule. Secondly, the item popularity was used to improve the Top-1 ranking probability. Then the stochastic gradient descent algorithm was used to solve the objective function and make Top- N recommendation. Based on the modified SVD++ rating rule, the proposed algorithm and the SVD++ algorithms whose objective functions are point-wise and list-wise were compared on MovieLens and Netflix datasets. Compared with the list-wise SVD++ algorithm, the value of Normalized Discounted Cumulative Gain (NDCG) of Top- N recommendation accuracy was increased by 5%-8% on MovieLens datasets and about 1% on Netflix datasets. The experimental results show that the proposed algorithm can effectively improve the Top- N recommendation accuracy.

In order to improve the accuracy and stability of indoor positioning, an indoor localization algorithm using particle filtering to fuse WiFi fingerprinting and Pedestrian Dead Reckoning (PDR) was proposed. To reduce the negative influence of complex indoor environment on WiFi fingerprinting, a Support Vector Machine (SVM)-based WiFi fingerprinting algorithm using SVM classification and regression for more accurate location estimation was proposed. For smartphone based PDR, in order to reduce the error of inertial sensor, and the effects of random walk, the method of state transition was used to recognize the gait cycles and count the steps, the parameters of state transition were set dynamically using real-time acceleration data, the step length was calculated with Kalman filtering by making use of the relationship between vertical acceleration and step size, and the relationship between adjacent step sizes. The experimental results show that SVM-based WiFi fingerprinting outperformed Nearest Neighbor (NN) algorithm by 34.4% and K-Nearest Neighbors ( KNN) algorithm by 27.7% in average error distance, the enhanced PDR performed better than typical step detection software and step length estimation algorithms. After particle filtering, the trajectory of the fused solution is closer to the real trajectory than WiFi fingerprinting and PDR. The average error distance of linear walking is 1.21 m, better than 3.18 m of WiFi and 2.76 m of PDR; the average error distance of a walking through several rooms is 2.75 m, better than 3.77 m of WiFi and 2.87 m of PDR.