Learning Resource Recommendation Method Based on Spatio-Temporal Multi-Granularity Interest Modeling

Zhu Haiping; Wang Ziyu; Zhao Chengcheng; Chen Yan; Liu Jun; Tian Feng

doi:10.7544/issn1000-1239.202440249

Journal of Computer Research and Development > 2024 > Accepted Manuscript > DOI: 10.7544/issn1000-1239.202440249 CSTR: 32373.14.issn1000-1239.202440249

Zhu Haiping, Wang Ziyu, Zhao Chengcheng, Chen Yan, Liu Jun, Tian Feng. Learning Resource Recommendation Method Based on Spatio-Temporal Multi-Granularity Interest Modeling[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202440249

Citation:

PDF (1955 KB)

Learning Resource Recommendation Method Based on Spatio-Temporal Multi-Granularity Interest Modeling

Zhu Haiping^{1, 2,},
Wang Ziyu¹,
Zhao Chengcheng¹,
Chen Yan^{1, 2},
Liu Jun^{1, 2},
Tian Feng^{1, 3, ,}

1.
School of Computer Science and Technology, Xi’an Jiaotong University, Xi'an 710049
2.
Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering (Xi’an Jiaotong University), Xi’an 710049
3.
National Engineering Lab for Big Data Analytics (Xi’an Jiaotong University), Xi’an 710049

Funds: This work was supported by the National Key Research and Development Program of China (2022YFC3303600), the National Natural Science Foundation of China (62277042,62293551,62177038,62377038), the Project of China Knowledge Centre for Engineering Science and Technology, and the “LENOVO-XJTU” Intelligent Industry Joint Laboratory Project.

More Information

Author Bio:
Zhu Haiping: born in 1974. PhD, associate professor, PhD supervisor, member of CCF. Her main research interests include big data analytics and recommender system

Wang Ziyu: born in 2000. Master candidate. His main research interests include recommender system and data mining

Zhao Chengcheng: born in 1997. Master. His main research interests include recommender system and data mining

Chen Yan: born in 1972. PhD, associate professor. Her main research interests include big data analytics, smart education and artificial intelligence

Liu Jun: born in 1973. PhD, professor, PhD supervisor. His main research interests include natural language understanding and computer vision

Tian Feng: born in 1972. PhD, professor, PhD supervisor. His main research interests include artificial intelligence and smart education
Received Date: April 02, 2024
Revised Date: October 22, 2024
Available Online: November 26, 2024

Graphical Abstract

Abstract

Abstract

Personalized learning resource recommendation is derived from identifying learners' interests and recommending interesting and relevant learning resources accordingly. However, learners’ interests are influenced by various factors such as knowledge points, learning resources, and courses, which makes it a challenging task to accurately represent their interests. Additionally, these interests evolve dynamically over time, complicating the task of identifying learning interest patterns. To address this challenge, we propose a learning resource recommendation method based on spatio-temporal multi-granularity interest modeling, which is characterized as follow: An innovative architecture is designed and implemented for learning interest representation that integrates the learning space and temporal dimension in a heterogeneous graph-based learning space and the multi-granularity interest representation. The nodes in this graph represent entities, such as knowledge points, learning resources, courses, teachers, and schools; and the edges of the graph represent the inter-entity relationships. A graph neural network is utilized to express the multi-granularity interest in these nodes. Moreover, we propose a temporal multi-granularity interest pattern representation method by combining multi-dimensionality of time, learning space, and course preference, and slicing through the sequence of learner's historical behaviors is used to mine the learner's different granularity of interest patterns in the near-term within-course, mid-term across-course, and long-term across-course. Then, a multi-granularity interest adaptive fusion layer is proposed to fuse multi-granularity interest representations and interest patterns. Based on this method a multi-granularity interest self-supervision task is designed to solve the problem of lack of supervised signaling for spatio-temporal multi-granularity interests, and recommend relevant learning resources for learners via prediction layer. Our experimental results show that on MOOCCube dataset the proposed method outperforms the optimal comparison algorithms HinCRec in Recall@20 and NDCG@20 metrics by 3.13% and 7.45%, respectively. On MOOPer dataset, the proposed method outperforms optimal comparison algorithm HinCRec in Recall@20 and NDCG@20 metrics by 4.87% and 7.03%, respectively.

FullText(HTML)

References (44)

References

[1]	da Silva FL, Slodkowski BK, da Silva KKA, et al. A systematic literature review on educational recommender systems for teaching and learning: research trends, limitations and opportunities[J]. Education and Information Technologies, 2023, 28(3): 3289−3328 doi: 10.1007/s10639-022-11341-9
[2]	Lin Yuanguo, Lin Fan, Zeng Wenhua, et al. Hierarchical reinforcement learning with dynamic recurrent mechanism for course recommendation[J]. Knowledge-Based Systems, 2022, 244(5): 1−15
[3]	Chen Qiwei, Zhao Huan, Li Wei, et al. Behavior sequence transformer for e-commerce recommendation in alibaba[C/OL]//Proc of the 1st Int Workshop on Deep Learning Practice for High-Dimensional Sparse Data(KDD). New York: ACM, 2019 [2024-09-09]. https://doi.org/10.1145/3326937.3341261
[4]	Sun Fei, Liu Jun, Wu Jian, et al. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer[C]//Proc of the 28th ACM Int Conf on Information and Knowledge Management(CIKM). New York: ACM, 2019: 1441−1450
[5]	陈碧毅,黄玲,王昌栋,等. 融合显式反馈与隐式反馈的协同过滤推荐算法[J]. 软件学报,2020,31(3):794−805 Chen Biyi, Huang Lin, Wang Changdong, et al. Explicit and implicit feedback based collaborative filtering algorithm[J]. Journal of Software, 2020, 31(3): 794−805 (in Chinese)
[6]	Zheng Yu, Gao Chen, Chang Jianxin, et al. Disentangling long and short-term interests for recommendation[C]//Proc of the ACM Web Conf 2022（WWW）. New York: ACM, 2022: 2256−2267 Zheng Yu,Gao Chen,Chang Jianxin,et al. Disentangling long and short-term interests for recommendation[C]//Proc of the ACM Web Conf 2022(WWW). New York:ACM,2022:2256−2267
[7]	Li Chao, Liu Zhiyuan, Wu Mengmeng, et al. Multi-interest network with dynamic routing for recommendation at Tmall[C]//Proc of the 28th ACM Int Conf on Information and Knowledge Management (CIKM). New York: ACM, 2019: 2615−2623
[8]	Xiao Zhibo, Yang Luwei, Jiang Wen, et al. Deep multi-interest network for click-through rate prediction[C]//Proc of the 29th ACM Int Conf on Information and Knowledge Management (CIKM). New York: ACM, 2020: 2265−2268
[9]	Cen Yukuo, Zhang Jianwei, Zou Xu, et al. Controllable multi-interest framework for recommendation[C]//Proc of the 26th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining (KDD). New York: ACM, 2020: 2942−2951
[10]	Guo Wei, Zhang Can, He Zhicheng, et al. Miss: Multi-interest self-supervised learning framework for click-through rate prediction[C]//Proc of the 38th Int Conf on Data Engineering. Piscataway, NJ: IEEE, 2022: 727−740
[11]	Ai Fangzhe, Chen Yishuai, Guo Yuchun, et al. Concept-aware deep knowledge tracing and exercise recommendation in an online learning system[J/OL]. International Educational Data Mining Society, 2019[2024-09-09]. https://eric.ed.gov/?id=ED599194
[12]	Huang Zhenya, Liu Qi, Zhai Chengxiang, et al. Exploring multi-objective exercise recommendations in online education systems[C]//Proc of the 28th ACM Int Conf on Information and Knowledge Management. New York: ACM, 2019: 1261−1270
[13]	Huo Yujia, Wong D F, Ni L M, et al. Knowledge modeling via contextualized representations for LSTM-based personalized exercise recommendation[J]. Information Sciences, 2020, 523:266−278 Huo Yujia,Wong D F,Ni L M,et al. Knowledge modeling via contextualized representations for LSTM-based personalized exercise recommendation[J]. Information Sciences, 2020, 523:266−278
[14]	Wu Zhengyang, Li Ming, Tang Yong, et al. Exercise recommendation based on knowledge concept prediction[J]. Knowledge-Based Systems, 2020, 210: 106481 Wu Zhengyang,Li Ming,Tang Yong,et al. Exercise recommendation based on knowledge concept prediction[J]. Knowledge-Based Systems,2020,210:106481
[15]	胡园园,姜文君,任德盛,等. 一种结合用户适合度和课程搭配度的在线课程推荐方法[J]. 计算机研究与发展,2022,59(11):2520−2533 doi: 10.7544/issn1000-1239.20210348 Hu Yuanyuan, Jiang Wenjun, Ren Desheng, et al. Integrating user duitability and course matching degree for online course recommendation method[J]. Journal of Computer Research and Development, 2022, 59(11): 2520−2533 (in Chinese) doi: 10.7544/issn1000-1239.20210348
[16]	Ren Xinwei, Yang Wei, Jiang Xianliang, et al. A deep learning framework for multimodal course recommendation based on LSTM+ attention[J]. Sustainability, 2022, 14(5): 2907−2921 doi: 10.3390/su14052907
[17]	Nafea SM,Siewe F,He Ying. On recommendation of learning objects using felder-silverman learning style model[J]. IEEE Access,2019,7:163034-163048(只有卷
[18]	Wan Shanshan, Niu Zhendong. A learner oriented learning recommendation approach based on mixed concept mapping and immune algorithm[J]. Knowledge-Based Systems, 2016, 103: 28−40 Wan Shanshan,Niu Zhendong. A learner oriented learning recommendation approach based on mixed concept mapping and immune algorithm[J]. Knowledge-Based Systems, 2016, 103: 28−40
[19]	Yan Lingyao, Yin Chuantao, Chen Hui, et al. Learning resource recommendation in e-learning systems based on online learning style[C]//Proc of the 14th Int Conf on Knowledge Science, Engineering and Management (KSEM). New York: ACM, 2021: 373−385
[20]	Gong Tuanji, Yao Xuanxia. Deep exercise recommendation model[J]. International Journal of Modeling and Optimization, 2019, 9(1): 18−23
[21]	刘雨. 基于图嵌入的学习资源序列推荐研究[D]. 西安:西安交通大学,2020 Liu Yu. A study on sequential recommendation of learning resources based on graph embedding[D]. Xi’an: Xi’an Jiaotong University, 2020 (in Chinese)
[22]	Gong Jibing, Wang Shen, Wang Jinlong, et al. Attentional graph convolutional networks for knowledge concept recommendation in moocs in a heterogeneous view[C]//Proc of the 43rd Int ACM SIGIR Conf on Research and Development in Information Retrieval(SIGIR). New York: ACM, 2020: 79−88
[23]	Wang Xinhua, Jia Linzhao, Guo Lei, et al. Multi-aspect heterogeneous information network for MOOC knowledge concept recommendation[J]. Applied Intelligence, 2022, 53(9): 1−15
[24]	Alatrash R, Chatti M A, Ain Q U, et al. ConceptGCN: Knowledge concept recommendation in MOOCs based on knowledge graph convolutional networks and SBERT[J]. Computers and Education: Artificial Intelligence, 2024, 6: 100193 Alatrash R,Chatti M A,Ain Q U,et al. ConceptGCN:Knowledge concept recommendation in MOOCs based on knowledge graph convolutional networks and SBERT[J]. Computers and Education:Artificial Intelligence, 2024, 6: 100193
[25]	Zhang Huanyu, Shen Xiaoxuan, Yi Baolin, et al. KGAN: Knowledge grouping aggregation network for course recommendation in MOOCs[J]. Expert Systems with Applications, 2023, 211: 118344 Zhang Huanyu,Shen Xiaoxuan,Yi Baolin,et al. KGAN:Knowledge grouping aggregation network for course recommendation in MOOCs[J]. Expert Systems with Applications,2023,211:118344
[26]	Chen Xin, Sun Yuhong, Zhou Tong, et al. A method on online learning video recommendation method based on knowledge graph[C/OL]//Proc of the 12th Int Conf on Intelligent Information Processing(IIP). 2022: 419−430 [2024-09-09]. https://link.springer.com/chapter/10.1007/978-3-031-03948-5_34#citeas
[27]	Zhou Guorui, Zhu Xiaoqiang, Song Chenru, et al. Deep interest network for click-through rate prediction[C]//Proc of the 24th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining(KDD). New York: ACM, 2018: 1059−1068
[28]	Zhou Guorui, Mou Na, Fan Ying, et al. Deep interest evolution network for click-through rate prediction[C]//Proc of the 33rd AAAI Conf on Artificial Intelligence. Palo Alto, CA: AAAI, 2019: 5941−5948
[29]	Cao Yixin, Wang Xiang, He Xiangnan, et al. Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences[C]//Proc of the Web Conf 2019（WWW）. New York: ACM, 2019: 151−161 Cao Yixin,Wang Xiang,He Xiangnan,et al. Unifying knowledge graph learning and recommendation:Towards a better understanding of user preferences[C]//Proc of the Web Conf 2019(WWW). New York:ACM,2019:151−161
[30]	Wang Xiang, Jin Hongye, Zhang An, et al. Disentangled graph collaborative filtering[C]//Proc of the 43rd Int ACM SIGIR Conf on Research and Development in Information Retrieval(SIGIR). New York: ACM, 2020: 1001−1010
[31]	Wang Xiang, Huang Tinglin, Wang Dingxian, et al. Learning intents behind interactions with knowledge graph for recommendation[C]//Proc of the Web Conf 2021（WWW）. New York: ACM, 2021: 878−887 Wang Xiang,Huang Tinglin,Wang Dingxian,et al. Learning intents behind interactions with knowledge graph for recommendation[C]//Proc of the Web Conf 2021(WWW). New York:ACM,2021:878−887
[32]	Qi Tao, Wu Fangzhao, Wu Chuhan, et al. HieRec: Hierarchical user interest modeling for personalized news recommendation[C/OL]//Proc of the 59th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: ACL, 2021: 5446−5456
[33]	Guo Jiayan, Yang Yaming, Song Xiangchen, et al. Learning multi-granularity consecutive user intent unit for session-based recommendation[C]//Proc of the 15th ACM Int Conf on Web Search and Data Mining(WSDM). New York: ACM, 2022: 343−352
[34]	Jiang Lu, Liu Kunpeng, Wang Yibin, et al. Reinforced explainable knowledge concept recommendation in MOOCs[J]. ACM Transactions on Intelligent Systems and Technology, 2023, 14(3): 1−20
[35]	Wu Shu, Tang Yuyuan, Zhu Yanqiao, et al. Session-based recommendation with graph neural networks[C]//Proc of the 33rd AAAI Conf on Artificial Intelligence. Palo Alto, CA: AAAI, 2019: 346−353
[36]	Hidasi B, Karatzoglou A, Baltrunas L, et al. Session-based recommendations with recurrent neural networks[J]. arXiv preprint, arXiv: 1511.06939, 2016
[37]	Tang Jiaxi, Wang Ke. Personalized top-n sequential recommendation via convolutional sequence embedding[C]//Proc of the 11th ACM Int Conf on Web Search and Data Mining. New York: ACM, 2018: 565−573
[38]	Liu Qiao, Zeng Yifu, Mokhosi R, et al. STAMP: Short-term attention/memory priority model for session-based recommendation[C]//Proc of the 24th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining(KDD). New York: ACM, 2018: 1831−1839
[39]	Yuan Fajie, Karatzoglou A, Arapakis I, et al. A simple convolutional generative network for next item recommendation[C]//Proc of the 12th ACM Int Conf on Web Search and Data Mining. New York: ACM, 2019: 582−590
[40]	Xu Chengfeng, Zhao Pengpeng, Liu Yanchi, et al. Graph contextualized self-attention network for session-based recommendation[C/OL]//Proc of the 28th Int Joint Conf on Artificial Intelligence. 2019: 3940−3946 [2024-09-09] . https://www.ijcai.org/proceedings/2019/0547.pdf
[41]	Fan Xinyan, Liu Zheng, Lian Jianxun, et al. Lighter and better: low-rank decomposed self-attention networks for next-item recommendation[C]//Proc of the 44th int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2021: 1733−1737
[42]	Hou Yupeng, Hu Binbin, Zhang Zhiqiang, et al. Core: Simple and effective session-based recommendation within consistent representation space[C]//Proc of the 45th Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2022: 1796−1801
[43]	Gong Jibing, Wan Yao, Liu Ye, et al. Reinforced moocs concept recommendation in heterogeneous information networks[J]. ACM Transactions on the Web, 2023, 17(3): 1−27
[44]	Van der Maaten L, Hinton G. Visualizing data using t-SNE[J/OL]. Journal of Machine Learning Research, 2008, 9(11) [2024-09-09]. https://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf?fbcl

[1]	Wu Tianxing, Cao Xudong, Bi Sheng, Chen Ya, Cai Pingqiang, Sha Hangyu, Qi Guilin, Wang Haofen. Constructing Health Management Information System for Major Chronic Diseases Based on Large Language Model[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202440570
[2]	Zhao Yun, Liu Dexi, Wan Changxuan, Liu Xiping, Liao Guoqiong. Mental Health Text Matching Model Integrating Characters’ Mental Portrait[J]. Journal of Computer Research and Development, 2024, 61(7): 1812-1824. DOI: 10.7544/issn1000-1239.202220987
[3]	Fu Tao, Chen Zhaojiong, Ye Dongyi. GAN-Based Bidirectional Decoding Feature Fusion Extrapolation Algorithm of Chinese Landscape Painting[J]. Journal of Computer Research and Development, 2022, 59(12): 2816-2830. DOI: 10.7544/issn1000-1239.20210830
[4]	Gan Xinbiao, Tan Wen, Liu Jie. Bidirectional-Bitmap Based CSR for Reducing Large-Scale Graph Space[J]. Journal of Computer Research and Development, 2021, 58(3): 458-466. DOI: 10.7544/issn1000-1239.2021.20200090
[5]	Zhou Donghao, Han Wenbao, Wang Yongjun. A Fine-Grained Information Diffusion Model Based on Node Attributes and Content Features[J]. Journal of Computer Research and Development, 2015, 52(1): 156-166. DOI: 10.7544/issn1000-1239.2015.20130915
[6]	Li Yaxiong, Zhang Jianqiang, Pan Deng, Hu Dan. A Study of Speech Recognition Based on RNN-RBM Language Model[J]. Journal of Computer Research and Development, 2014, 51(9): 1936-1944. DOI: 10.7544/issn1000-1239.2014.20140211
[7]	Huang He, Sun Yu'e, Chen Zhili, Xu Hongli, Xing Kai, Chen Guoliang. Completely-Competitive-Equilibrium-Based Double Spectrum Auction Mechanism[J]. Journal of Computer Research and Development, 2014, 51(3): 479-490.
[8]	Zhu Feng, Luo Limin, Song Yuqing, Chen Jianmei, Zuo Xin. Adaptive Spatially Neighborhood Information Gaussian Mixture Model for Image Segmentation[J]. Journal of Computer Research and Development, 2011, 48(11): 2000-2007.
[9]	Ma Xiao, Wang Xuan, and Wang Xiaolong. The Information Model for a Class of Imperfect Information Game[J]. Journal of Computer Research and Development, 2010, 47(12).
[10]	Ma Liang, Chen Qunxiu, and Cai Lianhong. An Improved Model for Adaptive Text Information Filtering[J]. Journal of Computer Research and Development, 2005, 42(1): 79-84.