Abstract

Recommending new items is an important, yet challenging problem due to the lack of preference history for the new items. To handle this problem, the existing system uses the popular core techniques like collaborative filtering, content-based filtering and combinations of these. In this paper, we propose a market-based approach for seeding recommendations for new items in which new items will reach the audience quickest. To support this approach we purposed the algorithm that match the new item specification (features) with the existing item and identify whether these features are available in existing item sets or not. The proposed system identifies the user opinion on new item feature those are available in existing item set and generates the quality report of newly launched item (which is not purchased yet).

References

• Andrew I. Schein, Alexandrin Popescul, Lyle H. Ungar, and David M. Pennock. Methods and metrics for cold-start recommendations. In SIGIR '02:Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pages 253–260, New York,NY, USA, 2002. ACM.
• X. N. Lam, T. Vu, T. D. Le, A. D. Duong. Addressing Cold-Start Problem in Recommenda-tion Systems. Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication (ICUIMC'08), New York, USA, 2008: 208-211.
• A Method to Solve Cold-Start Problem in Recommendation System based on Social Network Sub-community and Ontology Decision Modelm
• Recommender Systems, Prem Melville and Vikas Sindhwani IBM T. J. Watson Research Center, Yorktown Heights, NY 10598
• Daniel Billsus and Michael J. Pazzani. 1999. A hybrid user model for news story classification. In Proceedings of the seventh international conference on User modeling. 99–108.
• Manuel de Buenaga Rodr´?guez, Manuel J. Ma˜na L´opez, Alberto D´?az Esteban, and Pablo Gerv´as G´omezNavarro. 2001. A User Model Based on Content Analysis for the Intelligent Personalization of a News Service. In Proceedings of the 8th International Conference on User Modeling 2001 (UM '01). SpringerVerlag, London, UK, UK, 216–218. http://dl. acm. org/citation. cfm?id=647664. 733412
• E. Banos, I. Katakis, N. Bassiliades, G. Tsoumakas, and I. Vlahavas. 2006. PersoNews: a personalized news reader enhanced by machine learning and semantic filtering. In Proceedings of the 2006 Confederated international conference on On the Move to Meaningful Internet Systems: CoopIS, DOA, GADA, and ODBASE - Volume Part I (ODBASE'06/OTM'06). Springer-Verlag, Berlin, Heidelberg, 975–982. DOI:http://dx. doi. org/10. 1007/11914853 62
• Deepak Agarwal and Bee-Chung Chen. 2009. Regression-based latent factor models. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '09). ACM,19–28.
• Liang Zhang, Deepak Agarwal, and Bee-Chung Chen. 2011. Generalizing matrix factorization through flexible regression priors. In Proceedings of the fifth ACM conference on Recommender systems (RecSys '11). ACM, New York, NY, USA, 13–20. DOI:http://dx. doi. org/10. 1145/2043932. 2043940
• Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, Steffen Rendle, and Schmidt-Thie Lars. 2010. Learning Attribute-to-Feature Mappings for Cold-Start Recommendations. In Proceedings of the 2010 IEEE International Conference on Data Mining (ICDM '10). IEEE Computer Society, Washington, DC, USA, 176–185.
• W. W. Cohen, R. E. Schapire, and Y. Singer, "Learning to order things," Journal of Artificial Intelligence Research, 243{270, 1999.
• A. Popescul, L. H. Ungar, D. M. Pennock, and S. Lawrence, "Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments," Proceedings of the Seventeenth Conference on Uncertainty in Ariti¯cial Intelligence, 2001.
• R. Jin, L. Si, and C. Zhai, "A Study of Mixture Models for Collaborative Filtering," Journal of Information Retrieval, 2006.
• Park and Tuzhilin 2008] Park, Y. -J. and Tuzhilin, A. 2008. The long tail of recommender systems and how to leverage it. In Proc. of the 2008 ACM Conf. on Recommender systems. 11-18.
• S. -T. Park and W. Chu. Pairwise preference regression for cold-start recommendation. In RecSys'09, pages 21–28, 2009.
• K. Zhou, S. -H. Yang, and H. Zha. Functional matrix factorizations for cold-start recommendation. In SIGIR'11, pages 315–324, 2011.
• M. Sun, F. Li, J. Lee, K. Zhou, G. Lebanon, and H. Zha. Learning multiple-question decision trees for cold-start recommendation. In WSDM'13, pages 445–454, 2013.
• J. Lin, K. Sugiyama, M. -Y. Kan, and T. -S. Chua. Addressing cold-start in app recommendation: Latent user models constructed from twitter followers. In SIGIR'13, pages 283–293, 2013.
• Li, Q. , & Kim, B. M. (2003, October). Clustering approach for hybrid recommender system. In Web Intelligence, 2003. WI 2003. Proceedings. IEEE/WIC International Conference on (pp. 33-38). IEEE.
• J. B. Schafer, K. J. , and J. Riedl. "Recommender systems in ecommerce," In Proceedings of the ACM Conference on Electronic Commerce, 1999.
• B. Sarwar, G. Karypis, J. Konstan, and J. riedl, "Item-based collaborative filtering recommendation algorithm," WWW10, ACM, Hong Kong, May 1-5, 2001, pp. 285-295.
• M. Deshpande and G. Karypis, "Item-based top-N recom-mendations algorithms," ACM Transactions on Information Systems, vol. 22, no. 1,pp. 143–177, 2004.
• X. Y Su and T. M. Khoshgoftaar "A Survey of Collaborative Filtering Techniques," Advances in Artificial Intelligence, pp. 1-19, August 2009.
• Daoud, M. , Naqvi, S. K. , & Ahmad, A. (2014). Opinion Observer: Recommendation System on ECommerce Website. International Journal of Computer Applications, 105.
• Daoud, M. , Naqvi, S. K. , & Jha, A. N. Semantic Analysis of Context Aware Recommendation techniques.
• Daoud, M. , Naqvi, S. K. (2015). Recommendation System Techniques in Ecommerce System