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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (4): 39-48    DOI: 10.11925/infotech.2096-3467.2021.0683
Current Issue | Archive | Adv Search |
Measuring User Item Quality with Rating Analysis for Deep Recommendation Model
Zheng Xiao,Li Shuqing(),Zhang Zhiwang
College of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China
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Abstract  

[Objective] This paper designs a new deep learning algorithm to improve the recommendation results. [Methods] Our model evaluated user and item quality features from user ratings and item quality consistency, numerical distribution of ratings and time-period-based numerical distribution of ratings. [Results] We examined our model with the MovieLens dataset, and found the MAE and MSE were improved by up to 3.71% and 4.24%, respectively. [Limitations] More research is needed to explore a quality index evaluation method including attribute features of user and items. [Conclusions] The proposed model generates more accurate scoring prediction, and effectively improves the quality of recommendation.
Key words： Recommendation System      Deep Learning      User and Item Quality      Effective Features     
Received: 07 July 2021      Published: 12 May 2022
ZTFLH:  TP393  
Fund:Major Natural Science Research Projects of Colleges and Universities in Jiangsu Province of China(19KJA510011)
Corresponding Authors: Li Shuqing,ORCID：0000-0001-9814-5766     E-mail: leeshuqing@163.com
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Xiao Zheng
Shuqing Li
Zhiwang Zhang

Cite this article:

Zheng Xiao, Li Shuqing, Zhang Zhiwang. Measuring User Item Quality with Rating Analysis for Deep Recommendation Model. Data Analysis and Knowledge Discovery, 2022, 6(4): 39-48.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0683     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I4/39

Deep Recommendation Model Based on User and Item Quality Features
评分数值 1 2 3 4 5
频率 3 1 2 4 1
Example of Low Ratings Count and Variant Ratings
Beta分布策略 用户质量分数 MAE
U形α=β=0.5 2.90 1.21
钟形α=β=5 3.12 1.23
Result of Example of Low Ratings Count and Variant Ratings
评分数值 1 2 3 4 5
频率 0 0 3 7 1
Example of Low Ratings Count and Similar Ratings
Beta分布策略 用户质量分数 MAE
U形α=β=0.5 1.89 1.46
钟形α=β=5 1.95 1.42
Result of Example of Low Ratings Count and Similar Ratings
评分数值 1 2 3 4 5
频率 125 75 25 75 50
Example of High Ratings Count
Beta分布策略 用户质量分数 MAE
U形α=β=0.5 2.86 1.02
钟形α=β=5 3.03 1.01
Result of High Ratings Count
案例

评分数值		 1 2 3 4 5
案例1 评分频率 5 1 1 1 1
Mean Weights 0.566 0.111 0.111 0.111 0.111
NDR Weights 0.578 0.147 0.123 0.191 0.060
RAUQ_Beta Weights 0.511 0.022 0.027 0.043 0.396
案例2 评分频率 125 25 25 25 25
Mean Weights 0.566 0.111 0.111 0.111 0.111
NDR Weights 0.573 0.140 0.122 0.096 0.069
RAUQ_Beta Weights 0.590 0.168 0.134 0.083 0.025
Example of Weights per Level Generated Using Mean, NDR and RAUQ_Beta
评分时间 1 2 3 4 5
评分数量 799 620 21 8 8
平均评分 4.176 4.140 4.095 4 4.375
分配权重 0.549 0.426 0.015 0.005 0.005
Result of Average Rating Time for Item 1
稠密时间段评分数量 207 439 145 426 160
平均评分 4.169 4.202 4.134 4.117 4.206
分配权重 0.290 0.415 0.105 0.155 0.035
Result of Dense Rating Time Division for Item 1
算法 MAE MSE
ConvMF 0.747 0.842
NDRU 0.740 0.839
EPIR 0.735 0.834
RAUIQ_CORR 0.728 0.825
Results of User Quality
算法 MAE MSE
Average 0.755 0.860
HITS 0.750 0.851
ConvMF 0.745 0.842
RAUIQ_CORR 0.728 0.825
Results of Item Quality
算法 ML-100k ML-1M
Mean 0.805 0.776
WS 0.799 0.768
Correlation 0.781 0.755
LRMF 0.772 0.741
ConvMF 0.778 0.746
CDL 0.766 0.737
RAUIQ_CORR 0.761 0.728
RAUQ_Beta 0.757 0.725
Results of Each User Quality Algorithm
算法 ML-100k ML-1M
Average 0.790 0.755
HITS 0.781 0.750
ConvMF 0.778 0.746
RAUIQ_CORR 0.766 0.728
RAIQ_TIME 0.758 0.724
3 Results of Each Item Quality Algorithm
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