基于融合特征的LSTM评分预测

隐语义模型(LFM)能够有效地提取用户和对象的特征。本文基于LFM所提取的有效特征,提出一种基于融合特征的长短期记忆网络(LSTM)评分预测模型(F-LFM-LSTM)。首先,运用LFM模型提取用户和对象的有效特征。然后,融合用户的职业、年龄、性别标签和对象类别标签等辅助信息。最后,运用LSTM网络训练得到预测评分。通过在MovieLens100k数据集上实验表明,相比于几种得到较为广泛研究的算法,F-LFM-LSTM模型能够取得更好的评分预测效果。

LSTM Rating Prediction Based on Fusing Features

The Latent Factor Model (LFM) can effectively extract the features of users and items. In this paper, based on the effective feature extracted by LFM, we propose a fusing-feature based Long Short Term Memory network (LSTM) prediction model (F-LFM-LSTM). Firstly, we employ the LFM model to extract the effective features of users and items. Then, the users occupation, age, gender label and item category label are fused. Finally, the prediction rating is obtained by training the LSTM. Experiments on MovieLens100k dataset show that, compared with several widely discussed algorithms, the F-LFM-LSTM model has higher rating prediction accuracy.