Abstract:

Recommendation is an important means of dealing with information overload in the era of big data. Traditional recommendation algorithms have relatively low accuracy and reliability. Aiming at the cold start issue of new users and projects, we propose a hybrid recommendation algorithm   based on probability matrix decomposition (HR-TP). Firstly, the user's invisible trust relationship is mined from the perspective of the user’s rating. Then the label context is used to measure the relationship between items according to user characteristics. The relationship matrix is fused with the probability matrix model to make recommendation. Experiments show that the proposed method achieves better results in recommendation accuracy compared with conventional methods.

Key words: recommendation system, matrix decomposition, trust degree, project association