Aiming at the problems of data sparsity and inaccuracy of recommendation results in the traditional collaborative filtering algorithms, a Probability Matrix Factorization recommendation model based on Latent Dirichlet Allocations (LDA) and Convolutional Neural Network (CNN) named LCPMF was proposed, which considers the topic information and deep semantic information of project review document comprehensively. Firstly, the LDA topic model and the text CNN were used to model the project review document respectively. Then, the significant potential low-dimensional topic information and the global deep semantic information of project review document were obtained in order to capture the multi-level feature representation of the project document. Finally, the obtained features of users and multi-level projects were integrated into the Probability Matrix Factorization (PMF) model to generate the prediction score for recommendation. LCPMF was compared with the classical PMF, Collaborative Deep Learning (CDL) and Convolutional Matrix Factorization (ConvMF) models on the real datasets Movielens 1M, Movielens 10M and Amazon. The experimental results show that, compared to PMF, CDL and ConvMF models, on the Movielens 1M dataset, the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of the proposed recommender model LCPMF are reduced by 6. 03% and 5.38%, 5.12% and 4.03%, 1.46% and 2.00% respectively; on the Movielens 10M dataset, the RMSE and MAE of LCPMF are reduced by 5.35% and 5.67%, 2.50% and 3.64%, 1.75% and 1.74% respectively; while on the Amazon dataset, the RMSE and MAE of LCPMF are reduced by 17.71% and 23.63%, 14.92% and 17.47%, 3.51% and 4.87% respectively. The feasibility and effectiveness of the proposed model in the recommendation system are verified.

For the problems of low recommendation accuracy, data sparsity and malicious recommendation, a new recommendation model based on Probability Matrix Factorization (PMF) and fusing trust was proposed. Firstly, by establishing a Collaborative Filtering Model based on Trust Similarity (CFMTS), the improved trust mechanism was integrated into the collaborative filtering recommendation algorithm. The trust value was obtained through global trust and local trust calculation. The local trust was obtained by calculating the direct trust value and the indirect trust value of the user by the trust propagation mechanism, the global trust was calculated by the trust directed graph. Then, the trust value was combined with the score similarity to solve the problems of data sparsity and malicious recommendation. At the same time, CFMTS was integrated into the PMF model to establish a new recommendation model-Model based on Probability Matrix Factorization and Fusing Trust (MPMFFT). The user feature vectors and the project feature vectors were calculated by the gradient descent algorithm to generate the predicted scores, further improving the accuracy of the recommender system. Through experiments, the proposed MPMFFT was compared with the classical models such as PMF, Social Matrix Factorization (SocialMF), Social Recommendation (SoRec) and Recommendations with Social Trust Ensemble (RSTE). The proposed model has the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) decreased by 2.9% and 1.5% respectively compared with the optimal model RSTE on the open real dataset Epinions, and has the MAE and RMSE decreased by 1.1% and 1.8% respectively compared with the optimal SocialMF model on open real dataset Ciao, verifying that the proposed model is significantly improved on the above indicators. The results confirme that the propose model can resolve the problem of data sparseness and malicious recommendation to some extent, and effectively improved the recommendation quality.