Research on Recommendation Algorithm Based on TrustSVD++ and XGBoost

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Abstract

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%.

References

Chen P, Shu F, 2021, Trust Network Recommendation Algorithm Based on SVD++ Latent Factor Model. Big Data, 7(4): 105–116.

Zhang H, Ji H, Zhang H, 2017, Application of XGBoost Algorithm in E-commerce Commodity Recommendation. Internet of Things Technology, 7(2): 102–104.

He J, Hu J, 2021, Personalized Recommendation Algorithm Based on Matrix Factorization and XGBoost. Journal of Chongqing University, 44(1): 78–87.

Yin F, Song Y, Li A, 2019, Collaborative Filtering Algorithm Based on Local Optimization Singular Value Decomposition and K-means Clustering. Journal of Nanjing University of Science and Technology (Natural Science Edition), 43(6): 720–726.

Wang C, Li L, Liu X, 2018, Collaborative Filtering Algorithm Based on Explicit and Implicit Information. Information Systems Engineering, 2018(2): 147–148.

Zhang D, Zhang W, Zhang L, et al., 2021, Research on Singular Value Decomposition Recommendation Algorithm Based on Comprehensive Trust. High-tech Communication, 31(1): 102–112.

Du J, 2020, Deep Matrix Factorization Model Combined with User Reviews, dissertation, Huazhong University of Science and Technology.

Qiu Y, Wang X, Bian S, et al., 2020, Load Balancing Optimization of Multi-Frequency Multi-Mode Network Based on Clustering Analysis and Deep Learning. Telecommunication Science, 36(7): 156–162.

Cui Y, Qi W, Pang H, et al., 2020, Recommendation Algorithm Based on Collaborative Filtering and XG Boost. Application Research of Computers, 37(1): 62–65.

Liu X, 2018, Research on Internet Advertising Click-Through Rate Prediction Method Based on Deep Learning, dissertation, Xi’an University of Technology.

Li Z, 2021, News Recommendation Method Based on Improved Similarity and User Clustering, dissertation, Tianjin University of Commerce.

Song Y, 2021, Research on Group Recommendation System Integrating Social Trust Relationship, dissertation, Yantai University.

Liu Y, Cai X, Shen X, et al., 2019, Application of Collaborative Filtering Algorithm in Recommendation System. Information & Computer, 31(21): 38–40.

Li G, Li L, Zhang J, 2021, Social Collaborative Ranking Recommendation Algorithm Based on Explicit and Implicit Feedback. Journal of Computer Applications, 41(12): 3515–3520.

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