LSTM Rating Prediction Based on Fusing Features

doi:10.3969/j.issn.1006-2475.2020.03.010

Abstract

Abstract: 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.

Key words: latent factor model, F-LFM-LSTM model, rating prediction

CLC Number:

TP18

ZHANG Shang-tian, CHEN Guang, QIU Tian. LSTM Rating Prediction Based on Fusing Features[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2020.03.010.

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