基于随机游走和多样性图排序的个性化服务推荐方法

doi:10.3969/j.issn.0372-2112.2018.11.027

PDF(721 KB)

电子学报 ›› 2018, Vol. 46 ›› Issue (11) : 2773-2780. DOI: 10.3969/j.issn.0372-2112.2018.11.027

学术论文

基于随机游走和多样性图排序的个性化服务推荐方法

方晨^1,2, 张恒巍^1,2, 王娜¹, 王晋东^1,2

作者信息 +

Personalized Service Recommendation Method Based on Random Walk and Diversified Graph Ranking

FANG Chen^1,2, ZHANG Heng-wei^1,2, WANG Na¹, WANG Jin-dong^1,2

Author information +

文章历史 +

摘要

针对传统服务推荐算法由于数据稀疏性而导致推荐准确性不高，以及推荐结果缺乏多样性等缺陷，提出基于随机游走和多样性图排序的个性化服务推荐方法（PRWDR）.在分析直接相似关系稀疏性的基础上提出带权重的随机游走模型，通过在用户网络上进行随机游走来挖掘更多的相似关系；基于所有相似用户预测服务的QoS值，并给出服务图模型构建方法，以过滤大量性能过低的候选服务；提出最优节点集合选取策略，利用贪婪算法得到兼具推荐准确性和功能多样性的服务推荐列表.在公开发布的数据集上进行实验，并与多个经典算法进行比较，验证了本算法的有效性.

Abstract

In view of the low recommendation accuracy due to the sparseness of data, and the lack of diversity in traditional service recommendation algorithms, personalized service recommendation method based on random walking and diversified graph ranking (PRWDR) is proposed. On the basis of analyzing the sparseness of direct similarity relationships, a weighted random walk model is proposed, which can excavate more similarity relationships by random walk on the user network. The QoS value of services is predicted based on all similar users, and then the service graph model construction method is presented to filter those services with low performance. By using the greedy algorithm, the optimal node collection selection strategy is proposed to obtain the service recommendation list with both accuracy and diversity. By testing the algorithm on the public dataset and also comparing with several classic algorithms, the validity of PRWDR is verified.

导出引用

方晨, 张恒巍, 王娜, 王晋东. 基于随机游走和多样性图排序的个性化服务推荐方法[J]. 电子学报, 2018, 46(11): 2773-2780. https://doi.org/10.3969/j.issn.0372-2112.2018.11.027

FANG Chen, ZHANG Heng-wei, WANG Na, WANG Jin-dong. Personalized Service Recommendation Method Based on Random Walk and Diversified Graph Ranking[J]. Acta Electronica Sinica, 2018, 46(11): 2773-2780. https://doi.org/10.3969/j.issn.0372-2112.2018.11.027

中图分类号： TP393

参考文献

[1] 龙军,袁鑫攀,桂卫华.基于环境感知的可信QoS评价与服务选取策略[J].电子学报,2012,40(6):1133-1140. LONG Jun,YUAN Xin-pan,GUI Wei-hua.A policy for the trusted QoS evaluation and service selection with environment aware[J].Acta Electronica Sinica,2012,40(6):1133-1140.(in Chinese)
[2] 胡堰,彭启民,胡晓惠.一种基于隐语义概率模型的个性化Web服务推荐方法[J].计算机研究与发展,2014,51(8):1781-1793. HU Yan,PENG Qi-min,HU Xiao-hui.A personalized web service recommendation method based on latent semantic probabilistic model[J].Journal of Computer Research and Development,2014,51(8):1781-1793.(in Chinese)
[3] Liu X,Fulia I.Incorporating user,topic,and service related latent factors into web service recommendation[A].Proceedings of the IEEE International Conference on Web Services[C].New Work:IEEE,2015.185-192.
[4] 郭弘毅,刘功申,等.融合社区结构和兴趣聚类的协同过滤推荐算法[J].计算机研究与发展,2016,53(8):1664-1672. GUO Hong-yi,LIU Gong-shen,et al.Collaborative filtering recommendation algorithm combining community structure and interest clusters[J].Journal of Computer Research and Development,2016,53(8):1664-1672.(in Chinese)
[5] Zhang B,Li H,et al.Improving web search results using affinity graph[A].Proceedings of the ACM SIGIR International Conference on Research & Development in Information Retrieval[C].New Work:ACM,2005.504-511.
[6] Tong H,He J,Wen Z,et al.Diversified rankingon large graphs:An optimization viewpoint[A].Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining[C].New Work:ACM,2011.1028-1036.
[7] Li R H,Yu J X.Scalablediversified ranking on large graphs[J].IEEE Transactions on Knowledge & Data Engineering,2013,25(9):2133-2146.
[8] 俞春花,刘学军,李斌,等.基于上下文相似度和社会网络的移动服务推荐方法[J].电子学报,2017,45(6):1530-1536. YU Chun-hua,LIU Xue-jun,LI Bin,et al.Mobile service recommendation based on context similarity and social network[J].Acta Electronica Sinica,2017,45(6):1530-1536.(in Chinese)
[9] Bagci H,Karagoz P.Context-aware location recommendation by using a random walk-based approach[J].Knowledge and Information Systems,2016,47(2):241-260.
[10] Kang G,Liu J,Tang M,et al.AWSR:Active web service recommendation based on usage history[A].Proceedings of the International Conference on Web Services[C].Washington:IEEE Computer Society,2012.186-193.
[11] Maiya A S,Berger-Wolf T Y.Expansion and search in networks[A].Proceedings of the 19th ACM International Conference on Information and Knowledge Management[C].New York:ACM,2010.239-248.
[12] Grouplens.MovieLens[DB/OL].https://grouplens.org/datasets/movielens/.2017-12-31.
[13] Xie Q,Zhao S,Zheng Z,et al.Asymmetriccorrelation regularized matrix factorization for web service recommendation[A].Proceedings of the IEEE International Conference on Web Services[C].New Work:IEEE,2016.204-211.
[14] Aytekin T,Karakaya M Ö.Clustering-based diversity improvement in top-N recommendation[J].Journal of Intelligent Information Systems,2014,42(1):1-18.
[15] Usunier N,Usunier N,Grandvalet Y.Acoverage-based approach to recommendation diversity on similarity graph[A].Proceedings of the 10th ACM Conference on Recommender Systems[C].New York:ACM,2016.15-22.