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Data Analysis and Knowledge Discovery  2023, Vol. 7 Issue (12): 114-124    DOI: 10.11925/infotech.2096-3467.2022.1098
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A Deep Learning Recommendation Model with Item Audience Feature
Wang Yong,Chen Junyu,Liu Dong,Deng Jiangzhou()
School of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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Abstract  

[Objective] This paper proposes a deep learning recommendation model with item audience features. It captures collaborative information and the high-order features from users and items the interactions. [Methods] First,we used the attention mechanism to analyze the historical interaction information between items and users. Then, the system adaptively constructed personalized audience features of items. Third, we introduced these features to the model as important supplementary information for preference predictions. We also developed an explicit feature crossing and introduced residual connections to enrich the high-order features. [Results] We examined the new model with three public datasets. It improved the Precision, Recall, F1, and NDCG by up to 9.1%, 9.4%, 9.2%, and 12.1% compared with the sub-optimal method (the recommendation length = 10). [Limitations] The performance of our model relies mainly on the historical interaction data volumes. [Conclusions] The proposed model improves the recommendation quality and shows good application potential.
Key words： Attention Mechanism      Item Audience      Feature Crossing      Neural Networks      Recommendation Model     
Received: 20 October 2022      Published: 12 September 2023
ZTFLH:  TP391  
  G350  
Fund:National Natural Science Foundation of China(62272077);National Natural Science Foundation of China(72301050);Humanities and Social Sciences Research Project, the Ministry of Education(20YJAZH102)
Corresponding Authors: Deng Jiangzhou，ORCID：0000-0003-4761-132X，E-mail：dengjz@cqupt.edu.cn。   
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Yong Wang
Junyu Chen
Dong Liu
Jiangzhou Deng

Cite this article:

Wang Yong, Chen Junyu, Liu Dong, Deng Jiangzhou. A Deep Learning Recommendation Model with Item Audience Feature. Data Analysis and Knowledge Discovery, 2023, 7(12): 114-124.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.1098     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2023/V7/I12/114

Model Framework
Feature Interaction Layer with Two-Layer
数据集 用户数 项目数 评分数 稀疏度
ML-100k 943 1 682 100 000 93.7%
ML-1M 6 040 3 706 1 000 209 95.5%
YM 8 089 1 000 270 121 96.6%
Dataset Statistics
Performance Comparison of Variant Models
超参数\\数据集 ML-100k ML-1M YM
学习率 0.003 0.000 3 0.003
向量维度d 8 16 8
Dropout比率 0.2 0.4 0.4
正则项系数λ 0.08 0.05 0.08
神经元数量 48 96 48
Experimental Parameter Settings
Comparison of Model Performance
数据集 模型 NDCG@4 NDCG@6 NDCG@8 NDCG@10
ML-100k 本文模型 0.611 0.574 0.549 0.532
SimpleX 0.551 0.532 0.513 0.496
AFN 0.536 0.514 0.496 0.483
DCF 0.529 0.506 0.485 0.471
NCF 0.515 0.495 0.475 0.462
MF 0.475 0.460 0.446 0.431
ML-1M 本文模型 0.429 0.405 0.386 0.373
SimpleX 0.398 0.379 0.362 0.349
AFN 0.391 0.367 0.349 0.336
DCF 0.362 0.346 0.334 0.323
NCF 0.358 0.338 0.323 0.311
MF 0.348 0.326 0.311 0.298
YM 本文模型 0.183 0.165 0.153 0.142
SimpleX 0.158 0.144 0.135 0.127
AFN 0.139 0.129 0.121 0.114
DCF 0.122 0.115 0.109 0.104
NCF 0.113 0.107 0.102 0.097
MF 0.101 0.095 0.091 0.089
NDCG Values on Different Datasets
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