一种融合网络表示学习与XGBoost的评分预测模型

Predicting Online Ratings with Network Representation Learning and XGBoost

【目的】基于丰富的元数据和评分数据,提出一种融合网络表示学习与XGBoost的评分预测模型——N2V_XGB。【方法】提取并融合元数据和评分数据的相似性权重,构建同质关系网络;利用网络表示学习自动提取用户和项目特征,再将提取的特征作为XGBoost的输入,迭代训练获得最佳的评分预测模型。【结果】实验表明,N2V_XGB模型的MAE和RMSE分别为0.6867、0.8737,低于4种主要的对比模型。【局限】N2V_XGB模型未能很好地利用时间特征信息,评分结果没有反映时序变化。【结论】N2V_XGB模型将网络表示学习与XGBoost算法进行有效融合,能够缓解数据稀疏,提高用户评分的预测精度。

[Objective]This paper proposes a model to predict online ratings with the help of network representation learning and XGBoost—N2V_XGB.[Methods]First,we retrieved metadata and existing online rating data.Then,we extracted and merged the similarity weights of collected data to construct a homogenous relationship network.Third,we used network representation learning to automatically extract user and item features.Finally,we input these data to XGBoost,and obtained the best model with iteratively training.[Results]The MAE and RMSE of the proposed N2V_XGB model were 0.6867 and 0.8737,which were lower than the four classic models.[Limitations]We did not make good use of time features and the prediction results did not reflect time-series changes.[Conclusions]The proposed N2V_XGB model effectively address the data sparseness issues and improve the prediction accuracy of user ratings.