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2025 , Vol. 69 >Issue 20: 95 - 109

DOI: https://doi.org/10.13266/j.issn.0252-3116.2025.20.008

RESEARCH PAPERS

Session-Based Social Recommendation Method Based on Controllable Social Graph Learning

  • Zhang Xiongtao ,
  • Zhu Qinghua
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  • 1 School of Management, Hebei University, Baoding 071002;
    2 School of Information Management, Nanjing University, Nanjing 210023
Zhang Xiongtao, Lecturer, PhD, Postdoc; Zhu Qinghua, Professor, PhD, Doctoral Supervisor, Corresponding Author, E-mail: qhzhu@nju.edu.cn.

Received date: 2024-11-18

  Revised date: 2025-04-29

  Online published: 2025-09-29

Supported by

This work is supported by the project of Social Science Fund in Hebei Province, titled “Research on the Novelty Recommendation Methods of Social Media Integrating Users’ Variety Seeking” (Grant No. HB24TQ002).

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Abstract

[Purpose/Significance] The session-based social recommendation method has the advantages of both social recommendation and session-based recommendation, which is of great significance for enhancing personalized services on online social platforms. To address the limitations in controlling social influence, a session-based social recommendation method based on controllable social graph learning is proposed, which avoids the over-generalization of session-based social interest representation. [Method/Process] Firstly, sequence learning methods were adopted to learn the target user’s current session and the historical sessions of friend users, generating representations of the target user’s session interest and individual interests of friend users. Secondly, a controllable graph attention network was designed to learn the target user’s social relationships, integrating the user’s control of social influence to generate the socialized session interest representation. Lastly, by predicting the interaction probability between the socialized session interest and candidate items, the task of socialized session recommendation was accomplished. [Result/Conclusion] Two benchmark datasets from different domains of Douban community are used to verify the validity and rationality of the proposed method. The experimental results show that compared with the mainstream baselines, the proposed model has better recommendation performance across multiple metrics.

Key words： controllable social graph learning; session-based social recommendation; controllable graph attention network; sequence learning

Cite this article

Zhang Xiongtao , Zhu Qinghua . Session-Based Social Recommendation Method Based on Controllable Social Graph Learning[J]. Library and Information Service, 2025 , 69(20) : 95 -109 . DOI: 10.13266/j.issn.0252-3116.2025.20.008

References

[1] GAN M, ZHANG X, LIANG Y. CGG: Category-aware global graph contrastive learning for session-based recommendation[J]. Knowledge-based systems, 2024: 112661.
[2] 乌吉斯古楞, 王晰巍, 王楠阿雪. 智能推荐算法作用下重大突发事件群体信息从众行为研究——基于社会学习理论视角[J]. 图书情报工作, 2024, 68(20): 87-103. (WU J S G L, WANG X, WANG N A X. Research on group information herd behavior in major emergent events under the influence of intelligent recommendation algorithms: a social learning theory perspective[J]. Library and information service, 2024, 68(20): 87-103.)
[3] 张雄涛, 祝娜, 郭玉慧. 基于图神经网络的会话推荐方法综述[J]. 数据分析与知识发现, 2024, 8(2): 1-16. (ZHANG X T, ZHU N, GUO Y H. A survey on session-based recommendation methods with graph neural network[J]. Data analysis and knowledge discovery, 2024, 8(2): 1-16.)
[4] WANG D, LIANG Y, XU D, et al. A content-based recommender system for computer science publications[J]. Knowledge-based systems, 2018, 157: 1-9.
[5] ALHARBE N, RAKROUKI M A, ALJOHANI A. A collaborative filtering recommendation algorithm based on embedding representation[J]. Expert systems with applications, 2023, 215: 119380.
[6] CHEN W, NIU Z, ZHAO X, et al. A hybrid recommendation algorithm adapted in e-learning environments[J]. World Wide Web, 2014, 17: 271-284.
[7] 李洁, 张国标, 周毅, 等. 基于动态多任务学习的科技文献推荐模型构建及实证研究[J].图书情报工作, 2024, 68(13): 122-131. (LI J, ZHANG G B, ZHOU Y, et al. Construction and empirical study of scientific and technological literature recommendation model based on dynamic multi-task learning[J]. Library and information service, 2024, 68(13): 122-131.)
[8] XIA X, YU J, WANG Q, et al. Efficient on-device session-based recommendation[J]. ACM transactions on information systems, 2023, 41(4): 1-24.
[9] 许秦, 孟祥保, 顾建新. 社会化视频推荐书目特征及其动因研究——以哔哩哔哩“读书等身”活动为例[J]. 图书情报工作, 2021, 65(16): 56-64. (XU Q, MENG X B, GU J X. The characteristics and motivation of recommended bibliography of social video: a case analysis of “Du Shu Deng Shen” of Bilibili[J]. Library and information service, 2021, 65(16): 56-64.)
[10] LIU C, LI Y, LIN H, et al. GNNRec: Gated graph neural network for session-based social recommendation model[J]. Journal of intelligent information systems, 2023, 60(1): 137-156.
[11] 王丹, 付雨佳, 陈文锋. 社会情境对情绪感染的影响：一项基于EMG的超扫描研究[J]. 心理学报, 2024, 56(8): 1047-1060. (WANG D, FU Y J, CHEN W F. The influence of social context on emotional contagion: an EMG-based hyperscanning study[J]. Acta psychologica sinica, 2024, 56(8): 1047-1060.)
[12] LI J, REN P, CHEN Z, et al. Neural attentive session-based recommendation[C]// Proceedings of the 2017 ACM on conference on information and knowledge management. New York: Association for Computing Machinery, 2017: 1419-1428.
[13] LIU Q, ZENG Y, MOKHOSI R, et al. STAMP: Short-term attention/memory priority model for session-based recommendation[C]//Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. New York: Association for Computing Machinery, 2018: 1831-1839.
[14] WU S, TANG Y, ZHU Y, et al. Session-based recommendation with graph neural networks[C]//Proceedings of the AAAI conference on artificial intelligence. Washington, DC: AAAI Press, 2019, 33(1): 346-353.
[15] WANG Z, WEI W, CONG G, et al. Global context enhanced graph neural networks for session-based recommendation[C]//Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. New York: Association for Computing Machinery, 2020: 169-178.
[16] SONG W, XIAO Z, WANG Y, et al. Session-based social recommendation via dynamic graph attention networks[C]//Proceedings of the twelfth ACM international conference on web search and data mining. New York: Association for Computing Machinery, 2019: 555-563.
[17] 翟雨欣, 彭敦陆, 朱金玲. UniRec：融合项目表示一致性信息的会话推荐模型[J]. 小型微型计算机系统, 2025, 46(4): 856-862. (ZHAI Y, PENG D, ZHU J. UniRec: hybrid the uniformity of representation for session-based recommendation systems[J]. Journal of Chinese computer systems, 2025, 46(4): 856-862.)
[18] GAN M, WANG C, Yi L, et al. Exploiting dynamic social feedback for session-based recommendation[J]. Information processing & management, 2024, 61(3): 103632.
[19] CHEN T, WONG R C W. An efficient and effective framework for session-based social recommendation[C]//Proceedings of the 14th ACM international conference on web search and data mining. New York: Association for Computing Machinery, 2021: 400-408.
[20] WANG L, XU X, OUYANG K, et al. Self-supervised dual-channel attentive network for session-based social recommendation[C]//Proceedings of 38th international conference on data engineering. Kuala Lumpur: IEEE Computer Society, 2022: 2034-2045.
[21] WANG X, HE X, WANG M, et al. Neural graph collaborative filtering[C]//Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval. New York: Association for Computing Machinery, 2019: 165-174.
[22] WU S, SUN F, ZHANG W, et al. Graph neural networks in recommender systems: a survey[J]. ACM computing surveys, 2022, 55(5): 1-37.
[23] 石乐昊, 寇月, 申德荣, 等. 面向HIN基于多视角嵌入融合的推荐方法[J].软件学报, 2022, 33(10): 3619-3634. (SHI L H, KOU Y, SHEN D R, et al. Recommendation method based on multi-view embedding fusion for HINs[J]. Journal of software, 2022, 33(10): 3619-3634.)
[24] GUO Z, WANG H. A deep graph neural network-based mechanism for social recommendations[J]. IEEE transactions on industrial informatics, 2020, 17(4): 2776-2783.
[25] 唐宏, 范森, 唐帆. 融合协同知识图谱与优化图注意网络的推荐算法[J]. 计算机工程与应用, 2022, 58(19): 98-106. (TANG H, FAN S, TANG F. Recommendation algorithm integrating collaborative knowledge graph and optimizing graph attention network[J]. Computer engineering and applications, 2022, 58(19): 98-106.)
[26] 罗震, 张涛. 融合用户特征与知识图谱的可解释电影推荐方法[J]. 管理工程学报, 2024, 38(6): 128-139. (LUO Z, ZHANG T. Explainable movie recommendation method combining user features and knowledge graph[J]. Journal of industrial engineering and engineering management, 2024, 38(6): 128-139.)
[27] TAO Z, WEI Y, WANG X, et al. MGAT: Multimodal graph attention network for recommendation[J]. Information processing & management, 2020, 57(5): 102277.
[28] 刘京希, 邓曦泽, 曾军. 数智时代的人文危机与“新人文”建设[J]. 河北大学学报(哲学社会科学版), 2024, 49(1): 15-27. (LIU X, DENG X, ZENG J. The humanistic crisis in the age of digital intelligence and the construction of "new humanism" [J]. Journal of Hebei University (philosophy and social sciences edition), 2024, 49(1): 15-27.)
[29] ZHOU G, MOU N, FAN Y, et al. Deep interest evolution network for click-through rate prediction[C]//Proceedings of the AAAI conference on artificial intelligence. Washington, DC: AAAI Press, 2019, 33(1): 5941-5948.
[30] 琚春华, 赵凯迪, 鲍福光. 融入紧密度中心性与信用的社交网络用户影响力强度计算模型[J]. 情报学报, 2019, 38(2): 170-177. (JU C H, ZHAO K D, BAO F G. A user influence strength model in e-commerce social networks based on closeness and users’ credit[J]. Journal of the China Society for Scientific and Technical Information, 2019, 38(2): 170-177.)
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