To recommend useful microblogs that match users' interests and likes effectively,an approach in which the dynamic interests and social networking (DISN) of users are seamlessly integrated based on LDA model is proposed.The approach infers the interest vector of users better by using time function and groups the new published microblogs by clustering method and gets the best matching groups with users' interest vector.Then DISN traverses the selected groups by grid querying approach and matches the microblogs with publishers' probabilities of being followed and sorts the result.Finally the personalized microblogging recommendation is achieved.Experimental results show that DISN is more effective and efficient than the traditional models.
陈杰, 刘学军, 李斌, 章玮. 一种基于用户动态兴趣和社交网络的微博推荐方法[J]. 电子学报, 2017, 45(4): 898-905.
CHEN Jie, LIU Xue-jun, LI Bin, ZHANG Wei. Personalized Microblogging Recommendation Based on Dynamic Interests and Social Networking of Users. Acta Electronica Sinica, 2017, 45(4): 898-905.
[1] 高明,金澈清,钱卫宁,等.面向微博系统的实时个性化推荐[J].计算机学报,2014,37(4):963-975. Gao Ming,Jin Chen-qing,Qian Wei-ning,et al.Real-time and personalized recommendation on microblogging systems[J].Chinese Journal of Computers,2014,37(4):963-975.(in Chinese)
[2] Otsuka E,Wallace S A,Chiu D.Design and evaluation of a Twitter hashtag recommendation system[A].Proceedings of the 18th International Database Engineering & Applications Symposium[C].New York:ACM,2014.330-333.
[3] 张引,张斌,高克宁,等.面向自主意识的标签个性化推荐方法研究[J].电子学报,2012,40(12):2353-2359. Zhang Yin,Zhang Bin,GaoKe-ning,et al.Autonomy oriented personalized tag recommendation[J].Acta Electronica Sinica,2012,40(12):2353-2359.(in Chinese)
[4] Blei D M,Ng A Y,Jordan M I.Latent dirichlet allocation[J].Journal of Machine Learning Research,2003,3(5):993-1022.
[5] Ding,Yi,Xue Li.Time weight collaborative filtering[A].Proceedings of the 14th ACM International Conference on Information and Knowledge Management[C].Bremen,Germany:ACM,2005.485-492.
[6] Khater S,Elmongui H G,Gracanin D.Personalized microblogs corpus recommendation based on dynamic users interests[A].Proceedings of the 2013 International Conference on Social Computing[C].Washington:IEEE,2013.979-982.
[7] Wan S,Lan Y,Guo J,et al.Informational friend recommendation in social media[A].Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval[C].New York:ACM,2013.1045-1048.
[8] Kullback S,Leibler R A.On information and sufficiency[J].Annals of Mathematical Statistics,1951,22(1):79-86.
[9] Endres D M,Schindelin J E.A new metric for probability distributions[J].IEEE Transactions on Information Theory,2003,49(7):1858-1860.
[10] Dunn J C.A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters[J].Cybernetics and Systems,1973,3(3):32-57.
[11] Yang Z,Guo J,Cai K,et al.Understanding retweeting behaviors in social networks[A].Proceedings of the 19th ACM International Conference on Information and Knowledge Management[C].Toronto:ACM,2010.1633-1636.
[12] Wang C,Blei D M.Collaborative topic modeling for recommending scientific articles[A].Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining[C].San Diego,California:ACM,2011.448-456.
[13] Khater S,Elmongui H G.Tweets you like:Personalized tweets recommendation based on dynamic users interests[A].Proceedings of the ASE Big Data/Social Informatics/PASSAT/Bio Med Com 2014 Conference[C].USA:Harvard University,2014.1-10.
2017, Vol. 45 
