融合用户历史行为与社交关系的个性化社交事件推荐方法

doi:10.11772/j.issn.1001-9081.2020050666

计算机应用 ›› 2021, Vol. 41 ›› Issue (2): 324-329.DOI: 10.11772/j.issn.1001-9081.2020050666

所属专题：人工智能

融合用户历史行为与社交关系的个性化社交事件推荐方法

孙鹤立, 徐统, 何亮, 贾晓琳

西安交通大学电子与信息学部, 西安 710049

收稿日期:2020-05-19 修回日期:2020-07-20 出版日期:2021-02-10 发布日期:2020-09-15
通讯作者: 贾晓琳
作者简介:孙鹤立(1983-),女,辽宁铁岭人,副教授,博士,CCF会员,主要研究方向:数据挖掘、复杂网络分析;徐统(1994-),男,河南许昌人,硕士研究生,主要研究方向:社交网络、深度学习;何亮(1975-),男,陕西西安人,讲师,博士,CCF会员,主要研究方向:数据挖掘、机器学习;贾晓琳(1963-),女,陕西西安人,高级工程师,博士,CCF会员,主要研究方向:数据挖掘、机器学习。
基金资助:
国家自然科学基金资助项目（61672417）。

Personalized social event recommendation method integrating user historical behaviors and social relationships

SUN Heli, XU Tong, HE Liang, JIA Xiaolin

Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an Shaanxi 710049, China

Received:2020-05-19 Revised:2020-07-20 Online:2021-02-10 Published:2020-09-15
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61672417).

摘要/Abstract

摘要： 为了提升基于事件的社交网络（EBSN）中社交事件的推荐效果，提出了融合用户历史行为和社交关系的个性化社交事件推荐方法。首先采用深度学习技术从用户的历史行为以及用户之间的潜在社交关系两个方面建立用户模型；然后在对用户偏好建模时，引入用户偏好的负向量表示，并使用注意力权重层根据不同的候选推荐事件为用户历史行为中不同的事件和用户社交关系中不同的好友分配不同的权重，同时考虑了事件以及群组的多种特征；最后在真实数据集上进行了大量实验。实验结果表明，该个性化社交事件推荐方法在命中率（HR）、归一化折损累计增益（NDCG）、平均倒数排名（MRR）评价指标上优于对比的深度用户社交事件推荐（DUMER）模型和融合注意力机制的深度兴趣网络（DIN）模型。

关键词: 基于事件的社交网络, 深度学习, 个性化推荐方法, 注意力机制, 用户建模

Abstract: In order to improve the recommendation effect of social events in Event-based Social Network (EBSN), a personalized social event recommendation method combining historical behaviors and social relationships of users was proposed. Firstly, deep learning technology was used to build a user model from two aspects:the user's historical behaviors and the potential social relationships between users. Then, when modeling user preferences, the negative vector representation of user preferences was introduced, and the attention weight layer was used to assign different weights to different events in the user's historical behaviors and different friends in the user's social relationships according to different candidate recommendation events, at the same time, the various characteristics of events and groups were considered. Finally, a lot of experiments were carried out on the real datasets. Experimental results show that this personalized social event recommendation method is better than the comparative Deep User Modeling framework for Event Recommendation (DUMER) and DIN (Deep Interest Network) model combined with attention mechanism in terms of Hits Ratio (HR), Normalized Discounted Cumulative Gain (NDCG) and Mean Reciprocal Rank (MRR) evaluation indicators.

Key words: Event-Based Social Network (EBSN), deep learning, personalized recommendation method, attention mechanism, user modeling

中图分类号:

TP311

孙鹤立, 徐统, 何亮, 贾晓琳. 融合用户历史行为与社交关系的个性化社交事件推荐方法[J]. 计算机应用, 2021, 41(2): 324-329.

SUN Heli, XU Tong, HE Liang, JIA Xiaolin. Personalized social event recommendation method integrating user historical behaviors and social relationships[J]. Journal of Computer Applications, 2021, 41(2): 324-329.

参考文献

[1] LIU X,HE Q,TIAN Y,et al. Event-based social networks:linking the online and offline social worlds[C]//Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM,2012:1032-1040.
[2] ZHANG S,LV Q. Event organization 101:understanding latent factors of event popularity[C]//Proceedings of the 11th International AAAI Conference on Web and Social Media. Palo Alto,CA:AAAI Press,2017:716-719.
[3] YIN H,ZOU L,NGUYEN Q V H,et al. Joint event-partner recommendation in event-based social networks[C]//Proceedings of the IEEE 34th International Conference on Data Engineering. Piscataway:IEEE,2018:929-940.
[4] DU R,YU Z,MEI T,et al. Predicting activity attendance in eventbased social networks:content,context and social influence[C]//Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York:ACM,2014:425-434.
[5] PRAMANIK S,GUNDAPUNENI M,PATHAK S,et al. Can I foresee the success of my meetup group?[C]//Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Piscataway:IEEE, 2016:366-373.
[6] 杜蓉, 於志文, 刘振鲁, 等. 基于豆瓣同城活动的线上线下社交影响研究[J]. 计算机学报,2014,37(1):238-245.(DU R,YU Z W,LIU Z L,et al. Social influence of online and offline based on events[J]. Chinese Journal of Computers,2014,37(1):238-245.)
[7] PHAM T A N,LI X,CONG G,et al. A general graph-based model for recommendation in event-based social networks[C]//Proceedings of the IEEE 31st International Conference on Data Engineering. Piscataway:IEEE,2015:567-578.
[8] LIU S,WANG B,XU M. Event recommendation based on graph random walking and history preference reranking[C]//Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM,2017:861-864.
[9] MO Y,LI B,WANG B,et al. Event recommendation in social networks based on reverse random walk and participant scale control[J]. Future Generation Computer Systems,2018,79(Pt 1):383-395.
[10] LIU S, WANG B, XU M. SERGE:successive event recommendation based on graph entropy for event-based social networks[J]. IEEE Access,2017,6:3020-3030.
[11] 任星怡, 宋美娜, 宋俊德. 基于位置社交网络的上下文感知的兴趣点推荐[J]. 计算机学报,2017,40(4):824-841.(REN X Y, SONG M N, SONG J D. Context-aware point-of-interest recommendation in location-based social networks[J]. Chinese Journal of Computers,2017,40(4):824-841.)
[12] MACEDO A Q,MARINHO L B,SANTOS R L T. Context-aware event recommendation in event-based social networks[C]//Proceedings of the 9th ACM Conference on Recommender Systems. New York:ACM,2015:123-130.
[13] QIAO Z,ZHANG P,CAO Y,et al. Combining heterogenous social and geographical information for event recommendation[C]//Proceedings of the 28th AAAI Conference on Artificial Intelligence. Palo Alto,CA:AAAI Press,2014:145-151.
[14] LIAO G,ZHAO Y,XIE S,et al. An effective latent networks fusion based model for event recommendation in offline ephemeral social networks[C]//Proceedings of the 22nd ACM International Conference on Information and Knowledge Management. New York:ACM,2013:1655-1660.
[15] SHAN Y,HOENS T R,JIAO J,et al. Deep crossing:web-scale modeling without manually crafted combinatorial features[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM, 2016:255-262.
[16] CHENG H T,KOC L,HARMSEN J,et al. Wide & deep learning for recommender systems[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. New York:ACM, 2016:7-10.
[17] SONG W,SHI C,XIAO Z,et al. AutoInt:automatic feature interaction learning via self-attentive neural networks[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management. New York:ACM, 2019:1161-1170.
[18] VASWANI A,SHAZEER N,PARMAR N,et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook,NY:Curran Associates Inc.,2017:6000-6010.
[19] ZHOU G,ZHU X,SONG C,et al. Deep interest network for click-through rate prediction[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM,2018:1059-1068.
[20] WANG Z,ZHANG Y,CHEN H,et al. Deep user modeling for content-based event recommendation in event-based social networks[C]//Proceedings of the 2018 IEEE Conference on Computer Communications. Piscataway:IEEE, 2018:1304-1312.

融合用户历史行为与社交关系的个性化社交事件推荐方法

Personalized social event recommendation method integrating user historical behaviors and social relationships

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	李康康, 张静. 基于注意力机制的多层次编码和解码的图像描述模型[J]. 计算机应用, 2021, 41(9): 2504-2509.
[2]	赵宏, 孔东一. 图像特征注意力与自适应注意力融合的图像内容中文描述[J]. 计算机应用, 2021, 41(9): 2496-2503.
[3]	徐江浪, 李林燕, 万新军, 胡伏原. 结合目标检测的室内场景识别方法[J]. 计算机应用, 2021, 41(9): 2720-2725.
[4]	陈成瑞, 孙宁, 何世彪, 廖勇. 面向C-V2X通信的基于深度学习的联合信道估计与均衡算法[J]. 计算机应用, 2021, 41(9): 2687-2693.
[5]	谢德峰, 吉建民. 融入句法感知表示进行句法增强的语义解析[J]. 计算机应用, 2021, 41(9): 2489-2495.
[6]	代雨柔, 杨庆, 张凤荔, 周帆. 基于自监督学习的社交网络用户轨迹预测模型[J]. 计算机应用, 2021, 41(9): 2545-2551.
[7]	刘雅璇, 钟勇. 基于头实体注意力的实体关系联合抽取方法[J]. 计算机应用, 2021, 41(9): 2517-2522.
[8]	郑志强, 胡鑫, 翁智, 王雨禾, 程曦. 基于改进DenseNet的牛眼图像特征提取方法[J]. 计算机应用, 2021, 41(9): 2780-2784.
[9]	曹玉红, 徐海, 刘荪傲, 王紫霄, 李宏亮. 基于深度学习的医学影像分割研究综述[J]. 计算机应用, 2021, 41(8): 2273-2287.
[10]	秦斌斌, 彭良康, 卢向明, 钱江波. 司机分心驾驶检测研究进展[J]. 计算机应用, 2021, 41(8): 2330-2337.
[11]	何正海, 线岩团, 王蒙, 余正涛. 融合句法指导与字符注意力机制的案情阅读理解方法[J]. 计算机应用, 2021, 41(8): 2427-2431.
[12]	党伟超, 李涛, 白尚旺, 高改梅, 刘春霞. 基于自注意力长短期记忆网络的Web软件系统实时剩余寿命预测方法[J]. 计算机应用, 2021, 41(8): 2346-2351.
[13]	侯笑晗, 金国栋, 谭力宁, 薛远亮. 基于自适应和最优特征的合成孔径雷达舰船检测方法[J]. 计算机应用, 2021, 41(7): 2150-2155.
[14]	李朝, 兰海, 魏宪. 基于注意力的毫米波-激光雷达融合目标检测[J]. 计算机应用, 2021, 41(7): 2137-2144.
[15]	李亚芳, 梁烨, 冯韦玮, 祖宝开, 康玉健. 基于社区优化的深度网络嵌入方法[J]. 计算机应用, 2021, 41(7): 1956-1963.