%0 Conference Paper %1 Zhang:2016:GLI:2983323.2983873 %A Zhang, Qinzhe %A Wu, Jia %A Yang, Hong %A Lu, Weixue %A Long, Guodong %A Zhang, Chengqi %B Proceedings of the 25th ACM International on Conference on Information and Knowledge Management %C New York, NY, USA %D 2016 %I ACM %K social-influence-based-recommendation social-information-access %P 1917--1920 %R 10.1145/2983323.2983873 %T Global and Local Influence-based Social Recommendation %U http://doi.acm.org/10.1145/2983323.2983873 %X Social recommendation has been widely studied in recent years. Existing social recommendation models use various explicit pieces of social information as regularization terms in recommendation, for instance, social links are considered as new constraints. However, social influence, an implicit source of information in social networks, is seldomly considered, even though it often drives recommendations in social networks. In this paper, we introduce a new global and local influence-based social recommendation model. Based on the observation that user purchase behaviour is influenced by both global influential nodes and the local influential nodes of the user, we formulate the global and local influence as an regularization terms, and incorporate them into a matrix factorization-based recommendation model. Experimental results on large data sets demonstrate the performance of the proposed method. %@ 978-1-4503-4073-1

@inproceedings{Zhang:2016:GLI:2983323.2983873, abstract = {Social recommendation has been widely studied in recent years. Existing social recommendation models use various explicit pieces of social information as regularization terms in recommendation, for instance, social links are considered as new constraints. However, social influence, an implicit source of information in social networks, is seldomly considered, even though it often drives recommendations in social networks. In this paper, we introduce a new global and local influence-based social recommendation model. Based on the observation that user purchase behaviour is influenced by both global influential nodes and the local influential nodes of the user, we formulate the global and local influence as an regularization terms, and incorporate them into a matrix factorization-based recommendation model. Experimental results on large data sets demonstrate the performance of the proposed method.}, acmid = {2983873}, added-at = {2017-02-08T21:28:21.000+0100}, address = {New York, NY, USA}, author = {Zhang, Qinzhe and Wu, Jia and Yang, Hong and Lu, Weixue and Long, Guodong and Zhang, Chengqi}, biburl = {https://www.bibsonomy.org/bibtex/2e7e459db82acf65c432e4b10c51d1195/xianteng}, booktitle = {Proceedings of the 25th ACM International on Conference on Information and Knowledge Management}, description = {Global and Local Influence-based Social Recommendation}, doi = {10.1145/2983323.2983873}, interhash = {bb88c32078e7112b6319d0907e59e3e4}, intrahash = {e7e459db82acf65c432e4b10c51d1195}, isbn = {978-1-4503-4073-1}, keywords = {social-influence-based-recommendation social-information-access}, location = {Indianapolis, Indiana, USA}, numpages = {4}, pages = {1917--1920}, publisher = {ACM}, series = {CIKM '16}, timestamp = {2017-02-08T21:28:21.000+0100}, title = {Global and Local Influence-based Social Recommendation}, url = {http://doi.acm.org/10.1145/2983323.2983873}, year = 2016 }