社交网络中融合社交关系和语义信息的推荐算法

doi:10.16451/j.cnki.issn1003-6059.201803005

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (845 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要协同过滤方法广泛应用于推荐,但是数据稀疏成为模型提供高质量推荐的一大障碍.为了解决此问题,文中提出融合社交关系和语义信息的推荐算法,提高协同过滤方法的推荐性能,有机融合稀疏的用户行为记录、项目的社交信息和项目的语义信息.应用矩阵分解技术把行为矩阵和项目社交关系映射到一个低维的特征空间,提供项目社交关系信息分解的显式解释,分析关系信息对用户行为偏好产生的影响.同时,使用社会化因子正则的级联去噪自编码器模型学习项目语义特征,改进传统深度学习模型.在真实腾讯微博和Twitter数据集上的实验表明,文中方法有效提高召回率、准确率和推荐效率.

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	刘慧婷
	杨良全
	凌超
	赵鹏

关键词 ：推荐算法, 社交网络, 深度模型, 矩阵分解

Abstract：Collaborative filtering is a widely adopted approach in recommendation. However, sparse data remain the main obstacle to provide high quality recommendations. To address this issue, a method is proposed to improve the performance of collaborative filtering recommendations by integrating sparse action records data generated by users, the social information among items and the content information of these items. Matrix factorization technique is adopted to map the user action matrix and item social relations into the low-dimensional latent feature space to provide an explicit interpretation of factorization on item social relations and analyze the influence of social relations of item on user action preferences. Meanwhile, to learn more effective features from the item content, a social factor regularized stacked denoising autoencoder model is utilized and it is an extension of conventional deep learning model. Experimental results on the Tencent blog and Twitter datasets show that the proposed model outperforms several traditional methods in terms of recall and average precision, and it improves the recommendation efficiency effectively.

Key words： Recommendation Algorithm Social Network Deep Model Matrix Factorization

收稿日期: 2017-10-20

ZTFLH:

TP 311

基金资助:国家自然科学基金项目(No.61202227,61602004)资助

通讯作者: 杨良全,硕士研究生,主要研究方向为机器学习、数据挖掘.E-mail:15395602392@163.com.

作者简介: 刘慧婷,博士,副教授,主要研究方向为数据挖掘﹑机器学习.E-mail:htliu@ahu.edu.cn.凌超,硕士研究生,主要研究方向为机器学习、数据挖掘.E-mail:E15201002@gmail.com.赵鹏,博士,副教授,主要研究方向为智能信息处理、机器学习.E-mail:zhaopeng_ad@163.com.

引用本文:

刘慧婷, 杨良全, 凌超, 赵鹏. 社交网络中融合社交关系和语义信息的推荐算法[J]. 模式识别与人工智能, 2018, 31(3): 236-244. LIU Huiting, YANG Liangquan, LING Chao, ZHAO Peng. Recommendation Algorithm with Social Relations and Content Information in Social Networks. , 2018, 31(3): 236-244.

链接本文:

http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.201803005 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2018/V31/I3/236

[1] 陈婷,朱青,周梦溪,等.社交网络环境下基于信任的推荐算法.软件学报, 2017, 28(3): 721-731.
(CHEN T, ZHU Q, ZHOU M X, et al. Trust-Based Recommendation Algorithm in Social Network. Journal of Software, 2017, 28(3): 721-731.)
[2] KWAK H, LEE C, PARK H, et al. What Is Twitter, a Social Network or a News Media? // Proc of the 19th International Conference on World Wide Web. New York, USA: ACM, 2010: 591-600.
[3] ZIMMERMAN J, PARAMESWARAN L, KURAPATI K. Celebrity Recommender // Proc of the 2nd Workshop on Personalization in Future TV. Berlin, Germany: Springer, 2002: 39-47.
[4] WANG C, BLEI D M. Collaborative Topic Modeling for Recommending Scientific Articles // Proc of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2011: 448-456.
[5] MA H, YANG H X, LYU M R, et al. SoRec: Social Recommendation Using Probabilistic Matrix Factorization // Proc of the 17th ACM Conference on Information and Knowledge Management. New York, USA: ACM, 2008: 931-940.
[6] PURUSHOTHAM S, LIU Y, KUO C C J. Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems[C/OL]. [2017-08-30]. https://arxiv.org/ftp/arxiv/papers/1206/1206.4684.pdf.
[7] CHEN C C, ZHENG X L, WANG Y, et al. Context-Ware Collaborative Topic Regression with Social Matrix Factorization for Recommender Systems // Proc of the 28th AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2014: 9-15.
[8] CHEN K L, CHEN T Q, ZHENG G Q, et al. Collaborative Personalized Tweet Recommendation // Proc of the ACM SIGIR International Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2012: 661-670.
[9] WANG X, LU W, ESTER M, et al. Social Recommendation with Strong and Weak Ties // Proc of the 25th ACM International on Conference on Information and Knowledge Management. New York, USA: ACM, 2016: 5-14.
[10] ZHAO Z, LU H Q, CAI D, et al. User Preference Learning for Online Social Recommendation. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(9): 2522-2534.
[11] YANG B, LEI Y, LIU J M, et al. Social Collaborative Filtering by Trust. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(8): 1633-1647.
[12] JAMALI M, ESTER M. TrustWalker: A Random Walk Model for Combining Trust-Based and Item-Based Recommendation // Proc of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2009: 397-406.
[13] DING X T, JIN X M, LI Y J, et al. Celebrity Recommendation with Collaborative Social Topic Regression // Proc of the 23rd International Joint Conference on Artificial Intelligence. New York, USA: ACM, 2013: 2612-2618.
[14] NGIAM J, KHOSLA A, KIM M, et al. Multimodal Deep Learning // Proc of the 28th International Conference on Machine Learning. New York, USA: ACM, 2012: 689-696.
[15] BLEI D M, NG A Y, JORDAN M I. Latent Dirichlet Allocation. Journal of Machine Learning Research, 2003, 3: 993-1022.
[16] WANG H, WANG N Y, YEUNG D Y. Collaborative Deep Lear-ning for Recommender Systems // Proc of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2015: 1235-1244.
[17] YING H C, CHEN L, XIONG Y W, et al. Collaborative Deep Ranking: A Hybrid Pair-Wise Recommendation Algorithm with Implicit Feedback // Proc of the 20th Pacific-Asia Conference on Knowledge Discovery and Data Mining. Berlin, Germany: Sprin-ger, 2016: 555-567.
[18] LI S, KAWALE J, FU Y. Deep Collaborative Filtering via Margi-nalized Denoising Auto-Encoder // Proc of the 24th ACM International Conference on Information and Knowledge Management. New York, USA: ACM, 2015: 811-820.
[19] DENG S G, HUANG L J, XU G D, et al. On Deep Learning for Trust-Aware Recommendations in Social Networks. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(5): 1164-1177.
[20] ZHUANG F Z, ZHANG Z Q, QIAN M D, et al. Representation Learning via Dual-Autoencoder for Recommendation. Neural Networks, 2017, 90: 83-89.
[21] WEI J, HE J H, CHEN K, et al. Collaborative Filtering and Deep Learning Based Recommendation System for Cold Start Items. Expert Systems with Applications, 2017, 69: 29-39.
[22] DONG X, YU L, WU Z H, et al. A Hybrid Collaborative Filtering
Model with Deep Structure for Recommender Systems // Proc of the 31st AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2017: 1309-1315.
[23] HE X N, LIAO L Z, ZHANG H W, et al. Neural Collaborative Filtering // Proc of the 26th International Conference on World Wide Web. New York, USA: ACM, 2017: 173-182.
[24] GUO Z B, LI Z T, TU H. Sina Microblog: An Information-Driven Online Social Network // Proc of the International Conference on Cyberworlds. Washington, USA: IEEE, 2011: 160-167.
[25] KOREN Y, BELL R, VOLINSKY C. Matrix Factorization Techniques for Recommender Systems. Computer, 2009, 42(8): 30-37.
[26] PAN R, ZHOU Y H, CAO B, et al. One-Class Collaborative Filtering // Proc of the 8th IEEE International Conference on Data Mining. Washington, USA: IEEE, 2008: 502-511.
[27] VINCENT P, LAROCHELLE H, LAJOIE I, et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. Journal of Machine Learning Research, 2010, 11: 3371-3408.