Connecting Social Media to E-Commerce: Cold-Start Product Recommendation Using Microblogging Information

IEEE Transactions on Knowledge and Data Engineering

Volumn 28, Issue 5, 2016, Pages 1147-1159

  Zhao, Wayne Xin ^a,b   Li, Sui ^c   He, Yulan ^d   Chang, Edward Y ^e   Wen, Ji Rong ^a,b   Li, Xiaoming ^c  

^a RENMIN UNIVERSITY OF CHINA   (China)

^b Key Laboratory of Big Data Mining and Knowledge Management   (China)

^c PEKING UNIVERSITY   (China)

^d ASTON UNIVERSITY   (United Kingdom)

^e HTC CORPORATION   (Taiwan)

Author keywords

e commerce; microblogs; product demographic; product recommender; recurrent neural networks

Indexed keywords

COMMERCE; ELECTRONIC COMMERCE; FACTORIZATION; KNOWLEDGE MANAGEMENT; RECURRENT NEURAL NETWORKS; TREES (MATHEMATICS); WEBSITES;

FEATURE REPRESENTATION; MATRIX FACTORIZATIONS; MICRO-BLOGGING SERVICES; MICROBLOGS; PRODUCT DEMOGRAPHIC; PRODUCT RECOMMENDATION; PRODUCT RECOMMENDER; SOCIAL NETWORKING SITES;

SOCIAL NETWORKING (ONLINE);

EID: 84963812498     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2015.2508816     Document Type: Article

References (40)

1. J. Wang and Y. Zhang, "Opportunity model for E-commerce recommendation: Right product; right time," in Proc. 36th Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2013, pp. 303-312.
2. M. Giering, "Retail sales prediction and item recommendations using customer demographics at store level," SIGKDD Explor. Newsl., vol. 10, no. 2, pp. 84-89, Dec. 2008.
3. G. Linden, B. Smith, and J. York, "Amazon.com recommendations: Item-to-item collaborative filtering," IEEE Internet Comput., vol. 7, no. 1, pp. 76-80, Jan./Feb. 2003.
4. V. A. Zeithaml, "The new demographics and market fragmentation," J. Marketing, vol. 49, pp. 64-75, 1985.
5. W. X. Zhao, Y. Guo, Y. He, H. Jiang, Y. Wu, and X. Li, "We know what you want to buy: A demographic-based system for product recommendation on microblogs," in Proc. 20th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2014, pp. 1935-1944.
6. J. Wang, W. X. Zhao, Y. He, and X. Li, "Leveraging product adopter information from online reviews for product recommendation," in Proc. 9th Int. AAAI Conf. Web Social Media, 2015, pp. 464-472.
7. Y. Seroussi, F. Bohnert, and I. Zukerman, "Personalised rating prediction for new users using latent factor models," in Proc. 22nd ACM Conf. Hypertext Hypermedia, 2011, pp. 47-56.
8. T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, "Distributed representations of words and phrases and their compositionality," in Proc. Adv. Neural Inf. Process. Syst., 2013, pp. 3111-3119.
9. Q. V. Le and T. Mikolov, "Distributed representations of sentences and documents," CoRR, vol. abs/1405.4053, 2014.
10. J. Lin, K. Sugiyama, M. Kan, and T. Chua, "Addressing cold-start in app recommendation: Latent user models constructed from twitter followers," in Proc. 36th Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2013, pp. 283-292.
11. T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Efficient estimation of word representations in vector space," CoRR, vol. abs/1301.3781, 2013.
12. Y. Koren, R. Bell, and C. Volinsky, "Matrix factorization techniques for recommender systems," Computer, vol. 42, no. 8, pp. 30-37, Aug. 2009.
13. J. H. Friedman, "Greedy function approximation: A gradient boosting machine," Ann. Statist., vol. 29, pp. 1189-1232, 2000.
14. L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees. Monterey, CA, USA: Wadsworth & Brooks, 1984.
15. L. Breiman, "Random forests," Mach. Learn., vol. 45, no. 1, pp. 5-32, Oct. 2001.
16. K. Zhou, S. Yang, and H. Zha, "Functional matrix factorizations for Cold-start recommendation," in Proc. 34th Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2011, pp. 315-324.
17. T. Chen, H. Li, Q. Yang, and Y. Yu, "General functional matrix factorization using gradient boosting," in Proc. Int. Conf. Mach. Learn., 2013, pp. 436-444.
18. T. Chen, W. Zhang, Q. Lu, K. Chen, Z. Zheng, and Y. Yu, "SVDFeature: A toolkit for feature-based collaborative filtering," J. Mach. Learn. Res., vol. 13, pp. 3619-3622, 2012.
19. S. Rendle, "Factorization machines with libFM," ACM Trans. Intell. Syst. Technol., vol. 3, no. 3, May 2012.
20. S. Rendle, "Social network and Click-through prediction with factorization machines," in Proc. KDDCup Workshop, 2012.
21. B. Xiao and I. Benbasat, "E-commerce product recommendation agents: Use, characteristics, and impact," MIS Quart., vol. 31, pp. 137-209, 2007.
22. L. Hong, A. S. Doumith, and B. D. Davison, "Co-factorization machines: Modeling user interests and predicting individual decisions in twitter," in Proc. 6th ACM Int. Conf. Web Search Data Mining, 2013 pp. 557-566.
23. H. Ma, T. C. Zhou, M. R. Lyu, and I. King, "Improving recommender systems by incorporating social contextual information," ACM Trans. Inf. Syst., vol. 29, no. 2, 2011.
24. Y. Zhang, G. Lai, M. Zhang, Y. Zhang, Y. Liu, and S. Ma, "Explicit factor models for explainable recommendation based on Phraselevel sentiment analysis," in Proc. 37th Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2014, pp. 83-92.
25. M. Zhang, J. Tang, X. Zhang, and X. Xue, "Addressing cold start in recommender systems: A Semi-supervised Co-training algorithm," in Proc. 37th Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2014, pp. 73-82.
26. A. I. Schein, A. Popescul, L. H. Ungar, and D. M. Pennock, "Methods and metrics for cold-start recommendations," in Proc. 25th Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2002, pp. 253-260.
27. Y. Moshfeghi, B. Piwowarski, and J. M. Jose, "Handling data sparsity in collaborative filtering using emotion and semantic based features," in Proc. 34th Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2011, pp. 625-634.
28. H. Bao and E. Y. Chang, "AdHeat: An influence-based diffusion model for propagating hints to match ads," in Proc. 19th Int. Conf. World Wide Web, 2010, pp. 71-80.
29. N. N. Liu, X. Meng, C. Liu, and Q. Yang, "Wisdom of the better few: Cold start recommendation via representative based rating elicitation," in Proc. 5th ACM Conf. Recommender Syst., 2011, pp. 37-44.
30. S. J. Pan and Q. Yang, "A survey on transfer learning," IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345-1359, Oct. 2010.
31. W. Pan, E. W. Xiang, N. N. Liu, and Q. Yang, "Transfer learning in collaborative filtering for sparsity reduction," in Proc. AAAI, 2010, pp. 230-235.
32. A. P. Singh and G. J. Gordon, "Relational learning via collective matrix factorization," in Proc. 14th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2008, pp. 650-658.
33. B. Li, Q. Yang, and X. Xue, "Can movies and books collaborate? Cross-domain collaborative filtering for sparsity reduction," in Proc. 21st Int. Joint Conf. Artif. Intell., 2009, pp. 2052-2057.
34. L. Hu, J. Cao, G. Xu, L. Cao, Z. Gu, and C. Zhu, "Personalized recommendation via cross-domain triadic factorization," in Proc. 22nd Int. Conf. World Wide Web, 2013, pp. 595-606.
35. L. Zhao, S. J. Pan, E. W. Xiang, E. Zhong, Z. Lu, and Q. Yang, "Active transfer learning for cross-system recommendation," in Proc. 27th AAAI Conf. Artif. Intell., 2013, pp. 1205-1211.
36. B. Hollerit, M. Kröll, and M. Strohmaier, "Towards linking buyers and sellers: Detecting commercial intent on twitter," in Proc. 22nd Int. Conf. World Wide Web, 2013, pp. 629-632.
37. A. Mislove, B. Viswanath, K. P. Gummadi, and P. Druschel, "You are who you know: Inferring user profiles in online social networks," in Proc. 3rd ACM Int. Conf. Web Search Data Mining, 2010, pp. 251-260.
38. R. Zafarani and H. Liu, "Connecting corresponding identities across communities," in Proc. 3rd Int. Conf. Weblogs Social Media, 2009, pp. 354-357.
39. Y. Zhang and M. Pennacchiotti, "Recommending branded products from social media," in Proc. 7th ACM Conf. Recommender Syst., Hong Kong, China, Oct. 12-16, 2013, pp. 77-84.
40. Y. Zhang and M. Pennacchiotti, "Predicting purchase behaviors from social media," in Proc. 22nd Int. World Wide Web Conf., Rio de Janeiro, Brazil, May 13-17, 2013, pp. 1521-1532.