Crowdsourcing Task Recommendation Method Based on Collaborative Knowledge Graph and Hybrid Neural Network

doi:10.19678/j.issn.1000-3428.0070620

Abstract

Abstract:

In order to solve the problem of data sparsity in crowdsourcing task recommendation and improve the accuracy of crowdsourcing task recommendation, this paper proposes a crowdsourcing task recommendation method based on collaborative knowledge graph and hybrid neural network. This method first utilizes task entity alignment to fuse the worker—task bipartite graph and crowdsourcing task knowledge graph to form a worker task collaborative knowledge graph to alleviate the problem of data sparsity; Secondly, a Bidirectional Gated Recurrent Unit (BiGRU) is used to encode multiple paths between workers and tasks. Considering the correlation between paths, attention mechanism is utilized to weight and aggregate the information obtained from encoding multiple paths, in order to more accurately learn workers' preferences and recommend crowdsourcing tasks more accurately; At the same time, graph convolutional networks are used to capture the high correlation between crowdsourcing tasks and fully consider the complex semantic information of entities; Finally, based on the embedded representations of the workers and tasks obtained, provide comprehensive recommendations to the workers. The experimental results on six publicly available datasets, MovieLens-1M, Yelp, Book-Crossing, Music, Zhu-Bajie, and CHI, showed that compared with the benchmark model, the method proposed in this paper improved the Aera Under the ROC Curve (AUC) index by an average of 5.8%, 7.85%, 5.75%, 6.3%, 5.47%, and 4.58%, and the model also improved in other indicators. The experimental results demonstrate the effectiveness and stability of the proposed method, which can provide a research approach for the field of crowdsourcing task recommendation.

Key words: crowdsourcing task recommendation, collaborative knowledge graph, Bidirectional Gated Recurrent Unit (BiGRU), attention mechanism, graph convolutional network

摘要：

为了解决众包任务推荐存在的数据稀疏问题和提高众包任务推荐的准确性, 本文提出了一种基于协同知识图谱与混合神经网络的众包任务推荐方法。首先, 利用任务实体对齐融合工人-任务二分图与众包任务知识图谱, 形成工人-任务协同知识图谱, 以缓解数据稀疏性的问题; 其次, 采用双向门控循环单元(BiGRU)编码工人和任务之间的多条路径, 在考虑路径之间关联性的情况下利用注意力机制将编码多条路径得到的信息加权聚合, 以更准确地学习工人的偏好, 从而更加准确地推荐众包任务; 同时, 采用图卷积网络捕捉众包任务间的高相关性来充分考虑实体复杂的语义信息; 最后, 根据得到的工人和任务的嵌入表示向工人综合推荐。在MovieLens-1M、Yelp、Book-Crossing、Music、Zhu-Bajie和CHI 6个公开数据集上的实验结果显示, 与基准模型相比, 本文所提方法在ROC曲线下面积(AUC)指标方面平均提升了5.8%、7.85%、5.75%、6.3%、5.47%和4.58%, 在其他指标方面本文模型也均有提高。实验结果证明了本文方法的有效性与稳定性, 可以为众包任务推荐领域提供一个研究思路。

LI Jinru, PAN Qingxian, GAO Zhaolong, WEI Kai, FAN Zequn. Crowdsourcing Task Recommendation Method Based on Collaborative Knowledge Graph and Hybrid Neural Network[J]. Computer Engineering, 2026, 52(7): 155-167.

李金茹, 潘庆先, 高照龙, 魏凯, 范泽群. 基于协同知识图谱与混合神经网络的众包任务推荐方法[J]. 计算机工程, 2026, 52(7): 155-167.

/ Recommend / Download Citations

URL: https://www.ecice06.com/EN/10.19678/j.issn.1000-3428.0070620

https://www.ecice06.com/EN/Y2026/V52/I7/155

Figures/Tables 15

Fig.1 Overall framework design diagram of the model

Fig.2 Encoding path structure diagram of bidirectional gated attention mechanism

Fig.3 Graph convolutional network structure diagram

Fig.4 Comparison results of recall and precision metrics with benchmark models on MovieLens-1M dataset

Fig.5 Comparison results of recall and precision metrics with benchmark models on Yelp dataset

Fig.6 Comparison results of recall and precision metrics with benchmark models on Book-Crossing dataset

Fig.7 Comparison results of recall and precision metrics with benchmark models on the Music dataset

Fig.8 Comparison results of recall and precision metrics with benchmark models on Zhu-Bajie dataset

Fig.9 Comparison results of recall and precision metrics with benchmark models on CHI dataset

Fig.10 The impact of the number of correlation paths s between workers and crowdsourcing tasks on experimental results

References 33

1	沈旭, 王淑营, 田媛梦, 等. 基于知识图谱和图注意力的众包任务推荐算法. 计算机应用研究, 2023, 40 (1): 115- 121.
	SHEN X , WANG S Y , TIAN Y M , et al. Crowdsourced task recommendation algorithm based on knowledge graph and graph attention network. Application Research of Computers, 2023, 40 (1): 115- 121.
2	ABHINAV K, BHATIA G K, DUBEY A, et al. TasRec: a framework for task recommendation in crowdsourcing[C]//Proceedings of the 15th International Conference on Global Software Engineering. Washington D. C., USA: IEEE Press, 2020: 86-95.
3	LU K, WANG J X, LI M C, et al. Personalized and quality-aware task recommendation in collaborative crowdsourcing[C]//Proceedings of the IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD). Dalian, China: IEEE Press, 2021: 43-48.
4	WANG X , WANG D X , XU C R , et al. Explainable reasoning over knowledge graphs for recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33 (1): 5329- 5336. doi: 10.1609/aaai.v33i01.33015329
5	HUANG Y F , ZHAO F , GUI X Y , et al. Path-enhanced explainable recommendation with knowledge graphs. World Wide Web, 2021, 24 (5): 1769- 1789. doi: 10.1007/s11280-021-00912-4
6	徐静如, 董红斌, 赵炳旭, 等. 具有角色意识的社区服务型时空众包任务分配. 智能系统学报, 2023, 18 (2): 293- 304.
	XU J R , DONG H B , ZHAO B X , et al. Community service-oriented spatiotemporal crowdsourcing task allocation with role awareness. CAAI Transactions on Intelligent Systems, 2023, 18 (2): 293- 304.
7	孟祥福, 谢晶, 张峰. 考虑众包工人时空行为偏好的top-k任务推荐模型. 小型微型计算机系统, 2023, 44 (5): 974- 980.
	MENG X F , XIE J , ZHANG F . top-k task recommendation model considering spatio-temporal behavioral preferences of crowdsourced workers. Journal of Chinese Computer Systems, 2023, 44 (5): 974- 980.
8	ISMAILOGLU F . Aggregating user preferences in group recommender systems: a crowdsourcing approach. Decision Support Systems, 2022, 152, 113663. doi: 10.1016/j.dss.2021.113663
9	MIAO H , ZHONG X L , LIU J X , et al. Task assignment with efficient federated preference learning in spatial crowdsourcing. IEEE Transactions on Knowledge and Data Engineering, 2024, 36 (4): 1800- 1814. doi: 10.1109/TKDE.2023.3311816
10	GAO L , GAN Y , ZHOU B H , et al. A user-knowledge crowdsourcing task assignment model and heuristic algorithm for expert knowledge recommendation systems. Engineering Applications of Artificial Intelligence, 2020, 96, 103959. doi: 10.1016/j.engappai.2020.103959
11	HUANG W Y , LI P , LI B , et al. Towards stable task assignment with preference lists and ties in spatial crowdsourcing. Information Sciences, 2023, 620, 16- 30. doi: 10.1016/j.ins.2022.11.048
12	CHEN W , WAN H Y , GUO S N , et al. Building and exploiting spatial-temporal knowledge graph for next POI recommendation. Knowledge-Based Systems, 2022, 258, 109951. doi: 10.1016/j.knosys.2022.109951
13	GUO Q Y , ZHUANG F Z , QIN C , et al. A survey on knowledge graph-based recommender systems. IEEE Transactions on Knowledge and Data Engineering, 2022, 34 (8): 3549- 3568. doi: 10.1109/TKDE.2020.3028705
14	WANG H W, ZHANG F Z, WANG J L, et al. RippleNet: propagating user preferences on the knowledge graph for recommender systems[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management. New York, USA: ACM Press, 2018: 417-426.
15	ZHAO Y Y , WANG X , CHEN J W , et al. Time-aware path reasoning on knowledge graph for recommendation. ACM Transactions on Information Systems, 2023, 41 (2): 1- 26.
16	LIU J , HUANG W , LI T R , et al. Cross-domain knowledge graph chiasmal embedding for multi-domain item-item recommendation. IEEE Transactions on Knowledge and Data Engineering, 2023, 35 (5): 4621- 4633.
17	CHANG Y , ZHOU W , CAI H N , et al. Meta-relation assisted knowledge-aware coupled graph neural network for recommendation. Information Processing & Management, 2023, 60 (3): 103353.
18	PHAN H T , NGUYEN N T , HWANG D . Convolutional attention neural network over graph structures for improving the performance of aspect-level sentiment analysis. Information Sciences, 2022, 589, 416- 439. doi: 10.1016/j.ins.2021.12.127
19	LYU S , WANG K , WEI Y L , et al. GNN-based advanced feature integration for ICS anomaly detection. ACM Transactions on Intelligent Systems and Technology, 2023, 14 (6): 1- 32.
20	YANG B Y , WANG X , ZHANG S , et al. Joint modelling of task requirements and worker preferences based on heterogeneous features and multiple interactions for knowledge-intensive crowdsourcing recommendation. International Journal of Bio-Inspired Computation, 2023, 22 (2): 105- 116. doi: 10.1504/IJBIC.2023.134974
21	HE X N, DENG K, WANG X, et al. LightGCN: simplifying and powering graph convolution network for recommendation[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM Press, 2020: 639-648.
22	CHEN Z L , FU L L , YAO J , et al. Learnable graph convolutional network and feature fusion for multi-view learning. Information Fusion, 2023, 95, 109- 119. doi: 10.1016/j.inffus.2023.02.013
23	GAO X Y , FENG F L , HUANG H Y , et al. Food recommendation with graph convolutional network. Information Sciences, 2022, 584, 170- 183. doi: 10.1016/j.ins.2021.10.040
24	WANG X, HE X N, CAO Y X, et al. KGAT: knowledge graph attention network for recommendation[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York, USA: ACM Press, 2019: 950-958.
25	REN X H , CHEN T , NGUYEN Q V H , et al. Explicit knowledge graph reasoning for conversational recommendation. ACM Transactions on Intelligent Systems and Technology, 2024, 15 (4): 1- 21.
26	HE X N, CHUA T S. Neural factorization machines for sparse predictive analytics[C]//Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM Press, 2017: 355-364.
27	ZHANG F Z, YUAN N J, LIAN D F, et al. Collaborative knowledge base embedding for recommender systems[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM Press, 2016: 353-362.
28	WANG H W, ZHANG F Z, ZHAO M, et al. Multi-task feature learning for knowledge graph enhanced recommendation[C]//Proceedings of the World Wide Web Conference. New York, USA: ACM Press, 2019: 2000-2010.
29	TAI C Y, WU M R, CHU Y W, et al. Mvin: learning multiview items for recommendation[C]//Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. New York, USA: ACM Press, 2020: 99-108.
30	WANG Z, LIN G Y, TAN H B, et al. CKAN: collaborative knowledge-aware attentive network for recommender systems[C]// Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM Press, 2020: 219-228.
31	WANG J , YAN Y P , ZHAO G S . Task recommendation method combining multimodal cognition and collaboration in mobile crowdsensing systems. Computer Networks, 2023, 229, 109796. doi: 10.1016/j.comnet.2023.109796
32	LI X L , ZHANG L C , ZHOU M , et al. Task recommendation based on user preferences and user-task matching in mobile crowdsensing. Applied Intelligence, 2024, 54 (1): 131- 146. doi: 10.1007/s10489-023-05208-w
33	SHA X , SUN Z , ZHANG J . Hierarchical attentive knowledge graph embedding for personalized recommendation. Electronic Commerce Research and Applications, 2021, 48, 101071. doi: 10.1016/j.elerap.2021.101071

[1]	WU Di, ZHANG Le, CHEN Yao. Cross-Behavior Feature Regularization and Interaction Contrastive Learning Based Multi-Behavior Recommendation [J]. Computer Engineering, 2026, 52(7): 143-154.
[2]	LI Zhongwei, LI Keyi, LIU Xin. Sequential Recommendation Algorithm Based on Data Augmentation and Integrated Self-Attention [J]. Computer Engineering, 2026, 52(7): 94-106.
[3]	LIANG Ziyi, WANG Zihao, LIU Tianquan, LI Liping, ZHU Yuanfei, LU Cunyue. PASRGAN: A Super-Resolution Pathological Image Generation Algorithm Based on Parallel Attention Mechanism [J]. Computer Engineering, 2026, 52(7): 189-198.
[4]	ZHANG Hong, YANG Junyi. Spatio-Temporal Generative Adversarial Clustered Graph Convolutional Network for Anomalous Traffic Flow Prediction [J]. Computer Engineering, 2026, 52(7): 168-178.
[5]	JIN Jing, HU Chudi, CHEN Gang. Medical Image Segmentation Method Based on Multi-Scale Self-Attention Transformer [J]. Computer Engineering, 2026, 52(7): 199-209.
[6]	HE Mingyan, LI Siyuan, LIU Peng, HUANG Jianhua. Opponent Modeling Based on Cross-Attention Diffusion Model [J]. Computer Engineering, 2026, 52(7): 76-85.
[7]	LIU Xiangbin, FANG Cheng, LIU Shuai. Real-Time Semantic Segmentation Algorithm Based on Multi-Scale Feature Fusion and Polarized Self-Attention Mechanism [J]. Computer Engineering, 2026, 52(7): 210-219.
[8]	YANG Xinyi, HE Xiaohai, TENG Qizhi, CHEN Honggang, QING Linbo, WU Xiaohong. Cross-Modal Named Entity Recognition Based on Semantic Fusion [J]. Computer Engineering, 2026, 52(7): 345-353.
[9]	ZENG An, ZHENG Jiayu, PAN Dan, ZHAO Jingliang, HUANG Xingqing. Centerline Tracking Algorithm for Aortic Dissection Based on Deep Reinforcement Learning [J]. Computer Engineering, 2026, 52(6): 414-424.
[10]	YU Mengyuan, LIU Xiangyang. Ship Detection Method Based on Multimodal Visible and Infrared Image Fusion [J]. Computer Engineering, 2026, 52(6): 278-287.
[11]	WANG Yongqi, WANG Lei. Multimodal Sentiment Recognition Based on Cross-Modal Enhancement and Time-Step Gating [J]. Computer Engineering, 2026, 52(6): 258-267.
[12]	HU Kangyuan, GUO Tao, MU Nan. Image Inpainting Method for Cultural Relics Based on Self-Attention Mechanism and Dynamic Masking Mechanism [J]. Computer Engineering, 2026, 52(6): 179-188.
[13]	LUO Heng, WAN Liang. Network Traffic Intrusion Detection Method Based on Dynamic Spatio-Temporal Graph Neural Network [J]. Computer Engineering, 2026, 52(6): 202-213.
[14]	XIAO Zeqiu, LI Yong, WANG Xia. Prediction of Blood Glucose Concentration in Diabetic Patients Based on PBI-CLA Model [J]. Computer Engineering, 2026, 52(6): 382-390.
[15]	ZHOU Lijun, ZHANG Junran, WANG Kaiyuan, XIANG Junlian. Drug Recommendation Based on Graph-Enhanced Attention Integrating Clinical Features of Patients [J]. Computer Engineering, 2026, 52(6): 314-325.

Please choose a citation manager

Content to export