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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (12): 120-135    DOI: 10.11925/infotech.2096-3467.2020.0264
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Group Recommendation Algorithms Based on Implicit Representation Learning of Multi-attribute Ratings
Zhang Chunjin1,Guo Shenghui2,Ji Shujuan2(),Yang Wei2,Yi Lei2
1Network Security and Information Office, Shandong University of Science and Technology, Qingdao 266590, China
2Shandong Provincial Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and Technology, Qingdao 266590, China
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Abstract  

[Objective] This paper addresses the issues facing user representation learning due to the sparsity of their ratings, aiming to improve the performance of recommendation algorithm. [Methods] We proposed a neural network-based method to learn the implicit representation of multi-attribute ratings from user groups and individual items. Then, we conducted two group-oriented recommendations by matching their learned representations with preferences as well as calculating the attraction of each item. [Results] We examined our method with TripAdvisor data set and found the accuracy and time performance of the proposed algorithms were better than the typical multi-attribute ones and group ones. Compared to the personalized recommendation algorithm, the accuracies of our algorithms were slightly worse, but their online and offline running time was reduced by more than 30% and 50%, repectively. The recommendation results from user group based algorithm outperformed the item based one. [Limitations] We generated virtual groups based on clustering algorithm and their preferences were aggregated more effecitvely than the real world ones. [Conclusions] The proposed algorithms effectively improve the recommendation results.
Key words： Group Reccommendation Algorithms      Multi-attribute Ratings      Implicit Representation Learning      Rating Matrix      Nerual Network     
Received: 20 March 2020      Published: 25 December 2020
ZTFLH:  TP393  
Corresponding Authors: Ji Shujuan     E-mail: jsjsuzie@sina.com
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Chunjin Zhang
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Cite this article:

Zhang Chunjin,Guo Shenghui,Ji Shujuan,Yang Wei,Yi Lei. Group Recommendation Algorithms Based on Implicit Representation Learning of Multi-attribute Ratings. Data Analysis and Knowledge Discovery, 2020, 4(12): 120-135.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.0264     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I12/120

Schematic Diagram of the Framework
NNIRL Model
GMURec Algorithm Framework
GMIRec Algorithm Framework
属性 原始数据 预处理后数据
用户 536 952 20 443
项目 3 945 1 755
数据 796 958 262 300
稀疏度 99.96% 99.3%
Data Preprocessing
Silhouette Coefficient in Different Groups
参数 值 出现位置
梯度下降算法中的学习率α 0.15 公式(6)和公式(7)
梯度下降算法中误差阈值和迭代次数 0.01和100 000 梯度下降算法
调整因子σ 0.80 公式(9)和公式(11)
Parameter Settings
marcoF1of the Neural Network Structure in GMURec
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marcoF1of the Neural Network Structure in GMURec
marcoF1of the Neural Network Structure in GMIRec
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marcoF1of the Neural Network Structure in GMIRec
算法名称 简称 面向个体用户 面向群体用户 单属性 多属性
基于神经网络的群组推荐算法 ATTGRec √ √
基于最小痛苦策略的群组推荐算法 LP √ √
PromoRec算法 PromoRec √ √
基于Pearson相似度的协同过滤算法 PCCCF √ √
基于一种混合相似度的协同过滤算法 HyCF √ √
本文算法 GMURec、GMIRec √ √
The Proposed Algorithm and Baselines
Accuracy of the Algorithm and Baselines
Recall of the Algorithm and Baselines
Running Time of the Algorithm and Baselines
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