Research on Recommendation Algorithm Based on TrustSVD++ and XGBoost

Aiming at the problem that the data in the user rating matrix is missing and the importance of implicit trust between users is ignored when using the TrustSVD model to fill it, this paper proposes a recommendation algorithm based on TrustSVD++ and XGBoost. Firstly, the explicit trust and implicit trust were introduced into the SVD++ model to construct the TrustSVD++ model. Secondly, considering that there is much data in the interaction matrix after filling, which may lead to a rather complex calculation process, the K-means algorithm is introduced to cluster and extract user and item features at the same time. Then, in order to improve the accuracy of rating prediction for target users, an XGBoost model is proposed to train user and item features, and finally, it is verified on the data sets MovieLens-1M and MovieLens-100k. Experiments show that compared with the SVD++ model and the recommendation algorithm without XGBoost model training, the proposed algorithm has the RMSE value reduced by 2.9% and the MAE value reduced by 3%.