Associating Social Media to e-Merchandise - A Cold Start Commodity Recommendation
Ms. Habeebunissa Begum, Dr. G.S.S Rao, , ,
Affiliations Nawab ShahAlam khan College of Engineering and Technology, Hyd
:10.22362/ijcert/2017/v4/i10/xxxx [UNDER PROCESS]
In recent years, the boundaries between e-commerce and social networking became increasingly blurred. Many e-commerce websites support the mechanism of social login where users can sign up the websites victimization their social network identities like their Facebook or Twitter accounts. Users can also post their contemporary purchased product on microblogs with links to the e-commerce product websites. Throughout this paper, we incline to propose a novel declare a cross-site cold-start product recommendation that aims to advocate product from e-commerce websites to users at social networking sites in “cold start” things, a retardant that has rarely been explored before. A massive challenge may be thanks to leverage knowledge extracted from social networking sites for a cross-site cold-start product recommendation. We tend to propose to use the coupled users across social networking sites and e-commerce websites (user’s global organization agency have social networking accounts and have created purchases on e-commerce websites) as a bridge to map users’ social networking choices to a clear feature illustration for a product recommendation. In specific, we incline to propose learning every users’ and merchandises’ feature representations (called user embedding and merchandise embedding, respectively) from info collected from e-commerce websites victimization continual neural networks, therefore, apply a modified gradient boosting trees methodology to rework users’ social networking choices into user embedding. We incline to develop a feature-based matrix then resolving approach which could leverage the learned user embedding for a cold-start product recommendation. Experimental results on associate degree outsized dataset made of the most important Chinese microblogging service SINA WEIBO and conjointly the biggest Chinese B2C e-commerce website JINGDONG have shown the effectiveness of our planned framework.
Ms. Habeebunissa Begum.Dr. G.S.S Rao (2017). Associating Social Media to e-Merchandise - A Cold Start Commodity Recommendation: Study for Vehicular Ledger. International Journal of Computer Engineering In Research Trends, 4(10), 378-382. Retrieved from http://ijcert.org/ems/ijcert_papers/V4I1001.pdf
1] F. Cheng, C. Liu, J. Jiang, W. Lu, W. Li, G. Liu, W. Zhou, J. Huang, and Y. Tang. Prediction of drug-target interactions and drug repositioning via network-based inference.PLoS Computational Biology, 8:e1002503, 2012.
2] E. Constantinides. Influencing the online consumer’s behavior: the web experience.Internet research, 14:111–126, 2011.
3] J. L. Herlocker, J. A. Konstan, and J. Riedl.Explaining collaborative filtering recommendations. In Proceedings of the 2011 ACM conference on Computer supported cooperative work, pages 241–250. ACM,2011.
4] C. Jayawardhena, L. T. Wright, and C. Dennis. Consumers online: intentions, orientations and segmentation. International Journal of Retail &Distribution Management, 35:515–526, 2011.
5] A. Karatzoglou. Collaborative temporal order modeling. In Proceedings of the _fth ACM conferenceon Recommender systems, pages 313–316, 2009.
6] I. Konstas, V. Stathopoulos, and J. Jose. On social networks and collaborative recommendation.InProceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 195–202. ACM, 2007.
7] A. Liaw and M. Wiener.Classification and regression by randomforest.R news, 2:18–22, 2003.
8] C.-H. Park and Y.-G. Kim. Identifying key factors affecting consumer purchase behavior in an online shopping context.International Journal of Retail & Distribution Management, 31:16–29, 2002.
9] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: an open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, pages 175–186. ACM, 2001.
10] B. Sarwar, G. Karypis, J. Konstan, and J. Reidl.Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th InternationalConference on World Wide Web, pages 285–295. ACM, 2001.
11] J. B. Schafer, J. A. Konstan, and J. Riedl.E-commerce recommendation applications. In Applications of Data Mining to Electronic Commerce, pages 115–153. Springer, 2001.
12] E. Shen, H. Lieberman, and F. Lam. What am I gonna wear?: scenario-oriented recommendation. In Proceedings of the 12th international conference on intelligent user interfaces, pages 365–368. ACM, 2000.
13] K. H. Tso-Sutter, L. B. Marinho, and L. Schmidt-Thieme.Tag-aware recommender systems by fusion of collaborative filtering algorithms. In Proceedings of the 2008 ACM symposium on Applied computing, pages 1995–1999. ACM, 2008.
14] R. Verheijden. Predicting purchasing behavior throughout the clickstream.Master’s thesis, Eindhoven University of Technology, May 1994.
15] F. Wu and B. A. Huberman.Novelty and collective attention.Proceedings of the National Academy of Sciences, USA, 104:17599–17601.
16].K.Arun ,A.SrinageshandM.Ramesh,”Twitter Sentiment Analysis on Demonetization tweets in India Using R language.”International Journal of Computer Engineering in Research Trends., vol.4, no.6, pp. 252-258, 2017.
17] TekurVijetha, M.SriLakshmi and Dr.S.PremKumar,” Survey on Collaborative Filtering and content-Based Recommending.” International Journal of Computer Engineering in Research Trends., vol.2, no.9, pp. 594-599, 2015.
18] N.Satish Kumar, SujanBabuVadde,” Typicality Based Content-BoostedCollaborative Filtering Recommendation Framework.” International Journal of Computer Engineering in Research Trends., vol.2, no.11, pp. 809-813, 2015.
19] D.Ramanjaneyulu, U.Usha Rani,” In Service-Oriented MSN ProvidingTrustworthy Service Evaluation.”International Journal of Computer Engineering in Research Trends., vol.2, no.12, pp. 1192-1197, 2015.
20]B.Kundan,N.Poorna Chandra Rao and DrS.PremKumar,” Investigation on Privacy and Secure content of location based Queries.” International Journal of Computer Engineering in Research Trends., vol.2, no.9, pp. 543-546, 2015.
We have kept IJCERT is a free peer-reviewed scientific journal to endorse conservation. We have not put up a paywall to readers, and we do not charge for publishing. But running a monthly journal costs is a lot. While we do have some associates, we still need support to keep the journal flourishing. If our readers help fund it, our future will be more secure.