We use cookies to improve your experience with our site.

Indexed in:

SCIE, EI, Scopus, INSPEC, DBLP, CSCD, etc.

Submission System
(Author / Reviewer / Editor)
Advanced Search
Volume 37 Issue 6
December  2022
Turn off MathJax
Article Contents
Abstract
References
Related Articles
Supplements
Ming-Min Shao, Wen-Jun Jiang, Jie Wu, Yu-Qing Shi, TakShing Yum, Ji Zhang. Improving Friend Recommendation for Online Learning with Fine-Grained Evolving Interest[J]. Journal of Computer Science and Technology, 2022, 37(6): 1444-1463. DOI: 10.1007/s11390-021-2124-z
Citation: Ming-Min Shao, Wen-Jun Jiang, Jie Wu, Yu-Qing Shi, TakShing Yum, Ji Zhang. Improving Friend Recommendation for Online Learning with Fine-Grained Evolving Interest[J]. Journal of Computer Science and Technology, 2022, 37(6): 1444-1463. DOI: 10.1007/s11390-021-2124-z
shu

Improving Friend Recommendation for Online Learning with Fine-Grained Evolving Interest

  • 1College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
    2Department of Computer and Information Sciences, Temple University, Philadelphia 19122, U.S.A.
    3School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
    4Department of Information Engineering, Chinese University of Hong Kong, Hong Kong 999077, China
    5Department of Mathematics and Computing, University of Southern Queensland, Brisbane, QLD 4350, Australia

Funds: This research was supported by the National Natural Science Foundation of China under Grant Nos. 62172149, 61632009, 62172159, and 62172372, the Natural Science Foundation of Hunan Province of China under Grant No. 2021JJ30137, the Open Project of ZHEJIANG LAB under Grant No. 2019KE0AB02, and the Natural Science Foundation of Zhejiang Province of China under Grant No. LZ21F030001.
More Information
  • Author Bio:

    Wen-Jun Jiang received her Bachelor’s degree in computer science from Hunan University, Changsha, in 2004, her Master’s and Ph.D. degrees in computer software and theory from the Huazhong University of Science and Technology, Wuhan, and the Central South University, Changsha, in 2007 and 2014, respectively. Currently, she is a professor and Ph.D. supervisor of Hunan University, Changsha, and a senior member of CCF. Her main research interests include social network analysis and mining, recommendation system, and smart education and learning.

  • Corresponding author:

    Wen-Jun Jiang E-mail: jiangwenjun@hnu.edu.cn

  • Received Date: December 30, 2021
  • Revised Date: September 18, 2022
  • Accepted Date: October 29, 2022
  • Published Date: December 08, 2022
    Abstract
  • Friend recommendation plays a key role in promoting user experience in online social networks (OSNs). However, existing studies usually neglect users’ fine-grained interest as well as the evolving feature of interest, which may cause unsuitable recommendation. In particular, some OSNs, such as the online learning community, even have little work on friend recommendation. To this end, we strive to improve friend recommendation with fine-grained evolving interest in this paper. We take the online learning community as an application scenario, which is a special type of OSNs for people to learn courses online. Learning partners can help improve learners’ learning effect and improve the attractiveness of platforms. We propose a learning partner recommendation framework based on the evolution of fine-grained learning interest (LPRF-E for short). We extract a sequence of learning interest tags that changes over time. Then, we explore the time feature to predict evolving learning interest. Next, we recommend learning partners by fine-grained interest similarity. We also refine the learning partner recommendation framework with users’ social influence (denoted as LPRF-F for differentiation). Extensive experiments on two real datasets crawled from Chinese University MOOC and Douban Book validate that the proposed LPRF-E and LPRF-F models achieve a high accuracy (i.e., approximate 50% improvements on the precision and the recall) and can recommend learning partners with high quality (e.g., more experienced and helpful).
    • FullText(HTML)
    • References (50)
  • [1]
    Sakulwichitsintu S, Colbeck D, Ellis L et al. A peer learning framework for enhancing students’ learning experiences in online environments. In Proc. the 18th IEEE International Conference on Advanced Learning Technologies, July 2018, pp.168-169. DOI:  10.1109/ICALT.2018.00123.
    [2]
    Zewail-Foote M. Pivoting an upper-level, project-based biochemistry laboratory class to online learning during COVID-19: Enhancing research skills and using community outreach to engage undergraduate students. Journal of Chemical Education, 2020, 97(9): 2727-2732. DOI:  10.1021/acs.jchemed.0c00543.
    [3]
    Gong J B, Zhao Y, Chen S et al. Hybrid deep neural networks for friend recommendations in edge computing environment. IEEE Access, 2020, 8: 10693-10706. DOI:  10.1109/ACCESS.2019.2958599.
    [4]
    He C B, Li H C, Fei X et al. A topic community-based method for friend recommendation in large-scale online social networks. Concurrency and Computation: Practice and Experience, 2017, 29(6): Article No. e3924. DOI:  10.1002/cpe.3924.
    [5]
    Cheng Z Y, Ding Y, Zhu L et al. Aspect-aware latent factor model: Rating prediction with ratings and reviews. In Proc. the 2018 World Wide Web Conference, April 2018, pp.639-648. DOI:  10.1145/3178876.3186145.
    [6]
    Qi T, Wu F Z, Wu C H et al. HieRec: Hierarchical user interest modeling for personalized news recommendation. In Proc. the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, August 2021, pp.5446-5456. DOI:  10.18653/v1/2021.acl-long.423.
    [7]
    Wang H Z, Liu G F, Liu A et al. DMRAN: A hierarchical fine-grained attention based network for recommendation. In Proc. the 28th International Joint Conferences on Artificial Intelligence Organization, August 2019, pp.3698-3704. DOI:  10.24963/ijcai.2019/513.
    [8]
    Huang C L, Jiang W J, Wu J et al. Personalized review recommendation based on users’ aspect sentiment. ACM Transactions on Internet Technology, 2020, 20(4): Article No. 42. DOI:  10.1145/3414841.
    [9]
    Jiang W J, Chen J, Ding X F et al. Review summary generation in online systems: Frameworks for supervised and unsupervised scenarios. ACM Transactions on the Web, 2021, 15(3): Article No. 13. DOI:  10.1145/3448015.
    [10]
    Lima E, Shi W S, Liu X M et al. Integrating multi-level tag recommendation with external knowledge bases for automatic question answering. ACM Transactions on Internet Technology, 2019, 19(3): Article No. 34. DOI:  10.1145/3319528.
    [11]
    Shao M M, Jiang W J, Zhang L. FRFP: A friend recommendation method based on fine-grained preference. In Proc. the 7th International Conference on Smart City and Informatization, November 2019, pp.35-48. DOI:  10.1007/978-981-15-1301-5_4.
    [12]
    Wu B X, Xiao J, Chen J M. Friend recommendation by user similarity graph based on interest in social tagging systems. In Proc. the 11th International Conference on Intelligent Computing, August 2015, pp.375-386. DOI:  10.1007/978-3-319-22053-6_41.
    [13]
    Zhou T C, Ma H, Lyu M R, King I. UserRec: A user recommendation framework in social tagging systems. In Proc. the 24th AAAI Conference on Artificial Intelligence, July 2010, pp.1486-1491. DOI:  10.1609/aaai.v24i1.7524.
    [14]
    Bai T, Zhang Y, Wu B et al. Temporal graph neural networks for social recommendation. In Proc. the 2020 IEEE International Conference on Big Data, December 2020, pp.898-903. DOI:  10.1109/BigData50022.2020.9378444.
    [15]
    Fan W Q, Ma Y, Li Q et al. Graph neural networks for social recommendation. In Proc. the 2019 World Wide Web Conference, May 2019, pp.417-426. DOI:  10.1145/3308558.3313488.
    [16]
    Gong J B. Sequential recommendation through graph neural networks and transformer encoder with degree encoding. Algorithms, 2021, 14(9): Article No. 263. DOI:  10.3390/a14090263.
    [17]
    Guo L, Tang L, Chen T et al. DA-GCN: A domain-aware attentive graph convolution network for shared-account cross-domain sequential recommendation. In Proc. the 30th International Joint Conference on Artificial Intelligence, August 2021, pp.2483-2489. DOI:  10.24963/ijcai.2021/342.
    [18]
    Tao Y, Wang C, Yao L et al. Item trend learning for sequential recommendation system using gated graph neural network. Neural Computing and Applications, 2021. DOI:  10.1007/s00521-021-05723-2.
    [19]
    Devlin J, Chang M W, Lee K et al. BERT: Pre-training of deep bidirectional transformers for language understanding. https://arxiv.org/pdf/1810.04805.pdf, Nov. 2022.
    [20]
    Sun F, Liu J, Wu J et al. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proc. the 28th ACM International Conference on Information and Knowledge Management, November 2019, pp.1441-1450. DOI:  10.1145/3357384.3357895.
    [21]
    Sarwar B, Karypis G, Konstan J et al. Item-based collaborative filtering recommendation algorithms. In Proc. the 10th International Conference on World Wide Web, May 2001, pp.285-295. DOI:  10.1145/371920.372071.
    [22]
    Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. Computer, 2009, 42(8): 30-37. DOI:  10.1109/MC.2009.263.
    [23]
    He X N, Liao L Z, Zhang H W et al. Neural collaborative filtering. In Proc. the 26th International Conference on World Wide Web, April 2017, pp.173-182. DOI:  10.1145/3038912.3052569.
    [24]
    Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation, 1997, 9(8): 1735-1780. DOI:  10.1162/neco.1997.9.8.1735.
    [25]
    Yu F, Liu Q, Wu S et al. A dynamic recurrent model for next basket recommendation. In Proc. the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2016, pp.729-732. DOI:  10.1145/2911451.2914683.
    [26]
    Lv F Y, Jin T W, Yu C L et al. SDM: Sequential deep matching model for online large-scale recommender system. In Proc. the 28th ACM International Conference on Information and Knowledge Management, November 2019, pp.2635-2643. DOI:  10.1145/3357384.3357818.
    [27]
    Zhu Y, Li H, Liao Y K et al. What to do next: Modeling user behaviors by time-LSTM. In Proc. the 26th International Joint Conference on Artificial Intelligence, August 2017, pp.3602-3608. DOI:  10.24963/ijcai.2017/504.
    [28]
    Yu Z P, Lian J X, Mahmoody A et al. Adaptive user modeling with long and short-term preferences for personalized recommendation. In Proc. the 28th International Joint Conference on Artificial Intelligence, August 2019, pp.4213-4219. DOI:  10.24963/ijcai.2019/585.
    [29]
    Zhao W, Wang B Y, Ye J B et al. PLASTIC: Prioritize long and short-term information in top-n recommendation using adversarial training. In Proc. the 27th International Joint Conference on Artificial Intelligence, July 2018, pp.3676-3682. DOI: 10.24963/ijcai.2018/511.
    [30]
    Liu Q, Zeng Y F, Mokhosi R et al. STAMP: Short-term attention/memory priority model for session-based recommendation. In Proc. the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2018, pp.1831-1839. DOI:  10.1145/3219819.3219950.
    [31]
    Lei K, Fu Q A, Yang M et al. Tag recommendation by text classification with attention-based capsule network. Neurocomputing, 2020, 391: 65-73. DOI:  10.1016/j.neucom.2020.01.091.
    [32]
    Mihalcea R, Tarau P. TextRank: Bringing order into texts. In Proc. the 2004 Conference on Empirical Methods in Natural Language Processing, July 2004, pp.404-411.
    [33]
    Li S, Zhao Z, Hu R F et al. Analogical reasoning on Chinese morphological and semantic relations. In Proc. the 56th Annual Meeting of the Association for Computational Linguistics, July 2018, pp.138-143. DOI:  10.18653/v1/P18-2023.
    [34]
    Nahler G. Pearson correlation coefficient. In Dictionary of Pharmaceutical Medicine, Nahler G (ed.), Springer, 2009, pp.132. DOI:  10.1007/978-3-211-89836-9_1025.
    [35]
    Nir F, Stuart R. Image segmentation in video sequences: A probabilistic approach. In Proc. the 13th Conference on Uncertainty in Artificial Intelligence, August 1997, pp.175-181.
    [36]
    Hartigan J A, Wong M A. A K-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 1979, 28(1): 100-108. DOI: 10.2307/2346830.
    [37]
    Frey B J, Dueck D. Clustering by passing messages between data points. Science, 2007, 315(5814): 972-976. DOI:  10.1126/science.1136800.
    [38]
    Page L, Brin S, Motwani R et al. The PageRank citation ranking: Bringing order to the web. Technical Report, Stanford InfoLab, 1998. http://ilpubs.stanford.edu:8/422/1/1999-66.pdf, Jul. 2022.
    [39]
    Huo Y F, Chen B L, Tang J et al. Privacy-preserving point-of-interest recommendation based on geographical and social influence. Information Sciences, 2021, 543: 202-218. DOI:  10.1016/j.ins.2020.07.046.
    [40]
    Wang J, Che Y, Li D. Research on the relation-ship between students’ final grade and online learning behavior in blended learning model: Taking business Ethics and CSR course as an example. In Proc. the 2nd International Conference on Education, Knowledge and Information Management, January 2021, pp.159-166. DOI:  10.1109/ICEKIM52309.2021.00043.
    [41]
    Wang T C, Lee H D. Developing a fuzzy TOPSIS approach based on subjective weights and objective weights. Expert Systems with Applications, 2009, 36(5): 8980-8985. DOI:  10.1016/j.eswa.2008.11.035.
    [42]
    Rosenberg A, Hirschberg J. V-Measure: A conditional entropy-based external cluster evaluation measure. In Proc. the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, June 2007, pp.410-420.
    [43]
    Vinh N X, Epps J, Bailey J. Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance. Journal of Machine Learning Research, 2010, 11: 2837-2854.
    [44]
    Rousseeuw P J. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 1987, 20: 53-65. DOI:  10.1016/0377-0427(87)90125-7.
    [45]
    Lobo J, Jiménez-Valverde A, Real R. AUC: A misleading measure of the predictive distribution models. Global Ecology and Biogeography, 2008, 17(2): 145-151. DOI:  10.1111/j.1466-8238.2007.00358.x.
    [46]
    Goutte C, Gaussier R. A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In Proc. the 27th European Conference on Information Retrieval Research, March 2005, pp.345-359. DOI: 10.1007/978-3-540-31865-1_25.
    [47]
    Huang S R, Zhang J, Wang L et al. Social friend recommendation based on multiple network correlation. IEEE Transactions on Multimedia, 2016, 18(2): 287-299. DOI:  10.1109/TMM.2015.2510333.
    [48]
    Qader S A, Abbas A R. Dual-stage social friend recommendation system based on user interests. Iraqi Journal of Science, 2020, 61(7): 1759-1772. DOI:  10.24996/ijs.2020.61.7.25.
    [49]
    Yin Y, Feng X. Friend recommendation algorithm based on interest and cognition combined with feedback mechanism. In Proc. the 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation, August 2019, pp.1025-1030. DOI:  10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00199.
    [50]
    Usman U B, Umar K. Toward a hybrid technique for friends recommendation system in social Tagging. In Proc. the 3rd International Engineering Conference, September 2019, pp.404-410.
    • Relative Articles

  • Related Articles

    [1]Jia-Yu Liu, Fei Wang, Hai-Ping Ma, Zhen-Ya Huang, Qi Liu, En-Hong Chen, Yu Su. A Probabilistic Framework for Temporal Cognitive Diagnosis in Online Learning Systems[J]. Journal of Computer Science and Technology, 2023, 38(6): 1203-1222. DOI: 10.1007/s11390-022-1332-5
    [2]Jun-Wen Duan, Yi-Heng Chen, Ting Liu, Xiao Ding. Mining Intention-Related Products on Online Q&A Community[J]. Journal of Computer Science and Technology, 2015, 30(5): 1054-1062. DOI: 10.1007/s11390-015-1581-7
    [3]XU Zhiming, WANG Xiaolong. A New Linguistic Decoding Method for Online Handwritten Chinese Character Recognition[J]. Journal of Computer Science and Technology, 2000, 15(6): 597-604.
    [4]WU Jigang, JI Yongchang, CHEN Guoliang. An Optimal Online Algorithm for Half Plane Intersection[J]. Journal of Computer Science and Technology, 2000, 15(3): 295-299.
    [5]XI Haifeng, LUO Yupin, YANG Shiyuan. An Approach to Active Learning for Classifier Systems[J]. Journal of Computer Science and Technology, 1999, 14(4): 372-378.
    [6]Yao Shu, Zhang Bo. The Learning Convergence of CMAC in Cyclic Learning[J]. Journal of Computer Science and Technology, 1994, 9(4): 320-328.
    [7]Wu Xindong. Inductive Learning[J]. Journal of Computer Science and Technology, 1993, 8(2): 22-36.
    [8]Harald E. Otto. UNDO, An Aid for Explorative Learning?[J]. Journal of Computer Science and Technology, 1992, 7(3): 226-236.
    [9]Hayong Zhou. Analogical Learning and Automated Rule Constructions[J]. Journal of Computer Science and Technology, 1991, 6(4): 316-328.
    [10]Shi Weigeng, StephenY.H.Su. An Online Diagnosable Fault-Tolerant Redundancy System[J]. Journal of Computer Science and Technology, 1987, 2(4): 310-321.
    • Supplements (2)

  • Others

  • Cited by

    Periodical cited type(5)

    1. Chaoyong Wei, Jiang Wenjun, Kenli Li, et al. Stability-aware Preference Modeling for Sequential Recommendation. ACM Transactions on the Web, 2025. DOI:10.1145/3715147
    2. Zhaoyu Shou, Yixin Chen, Hui Wen, et al. Research on Joint Recommendation Algorithm for Knowledge Concepts and Learning Partners Based on Improved Multi-Gate Mixture-of-Experts. Electronics, 2024, 13(7): 1272. DOI:10.3390/electronics13071272
    3. Ding Ding, Jing Yi, Jiayi Xie, et al. Meta-path aware dynamic graph learning for friend recommendation with user mobility. Information Sciences, 2024, 666: 120448. DOI:10.1016/j.ins.2024.120448
    4. Fattane Zarrinkalam, Havva Alizadeh Noughabi, Zeinab Noorian, et al. Predicting users’ future interests on social networks: A reference framework. Information Processing & Management, 2024, 61(5): 103765. DOI:10.1016/j.ipm.2024.103765
    5. Jing Chen, Wenjun Jiang, Jie Wu, et al. Dynamic Personalized POI Sequence Recommendation with Fine-Grained Contexts. ACM Transactions on Internet Technology, 2023, 23(2): 1. DOI:10.1145/3583687

    Other cited types(0)

Catalog

    Get Citation
    PDF
    XML
    Read Online
    Article views (118) PDF downloads (16) Cited by(5)
    Related

    Copyright ©2025 JCST, All Rights Reserved     京ICP备05002829号-1

    Institute of Computing Technology, Chinese Academy of Sciences
    P.O. Box 2704, Beijing 100190, P.R. China

    Tel.: 86-10-62610746 E-mail: jcst@ict.ac.cn

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return