融合评论文本特征和评分图卷积表示的推荐模型

doi:10.11959/j.issn.1000-436x.2022049

Abstract

Abstract:

In order to fully exploit the effective information of the ratings and further investigate the importance of the review, a recommendation model combining review’s feature and rating graph convolutional representation was proposed.Graph convolutional neural network was used to learn the representation of user and item from the ratings data.Combining with text convolutional features, attention mechanism was utilized to distinguish the importance of the review.Finally, the representation learned from the review and the rating data was fused by the hidden factor model.The experimental results on Amazon’s public data showed that the proposed model significantly outperformed the traditional approaches, proving the effectiveness of the proposed model.

Key words: recommender model, graph convolutional encoder, attention mechanism, latent factor model

CLC Number:

TP391

Hailin FENG, Xiao ZHANG, Tongcun LIU. Recommendation model combining review’s feature and rating graph convolutional representation[J]. Journal on Communications, 2022, 43(3): 164-171.

Figures/Tables 6

References 25

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数据集	用户数	商品数	评分数
MI	1 429	900	10 261
PLG	1 686	962	13 272
Auto	2 928	1 835	20 473
AIV	5 130	1 685	37 126

推荐模型	评分数据	评论文本	卷积神经网络	注意力机制	图卷积编码
PMF	3	—	—	—	—
LFM	3	—	—	—	—
ConvMF	3	3	3	—	—
DeepCoNN	—	3	3	—	—
NARRE	—	3	3	3	—
GC-MC	3	—	—	—	3
CRRG	3	3	3	3	3

推荐模型	MI	PLG	Auto	AIV
PMF	1.363 6	1.492 3	1.506 4	1.424 5
LFM	1.792 0	1.671 9	1.798 6	1.485 8
ConvMF	1.357 4	1.331 8	1.283 4	1.228 3
DeepCoNN	0.835 7	1.070 9	0.835 5	1.018 8
NARRE	0.804 2	1.025 2	0.788 5	0.961 2
GC-MC	1.129 9	1.292 4	1.024 3	1.151 5
CRRG	0.791 4	1.017 1	0.785 9	0.959 8

Recommendation model combining review’s feature and rating graph convolutional representation

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