Research Article
Product Recommendations System Survey
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 131 - Issue 9 |
| Published: December 2015 |
| Authors: Sahil Pathan, Karan Panjwani, Nitin Yadav, Shreyas Lokhande, Bhushan Thakare |
10.5120/ijca2015907394
|
Sahil Pathan, Karan Panjwani, Nitin Yadav, Shreyas Lokhande, Bhushan Thakare . Product Recommendations System Survey. International Journal of Computer Applications. 131, 9 (December 2015), 36-38. DOI=10.5120/ijca2015907394
@article{ 10.5120/ijca2015907394,
author = { Sahil Pathan,Karan Panjwani,Nitin Yadav,Shreyas Lokhande,Bhushan Thakare },
title = { Product Recommendations System Survey },
journal = { International Journal of Computer Applications },
year = { 2015 },
volume = { 131 },
number = { 9 },
pages = { 36-38 },
doi = { 10.5120/ijca2015907394 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2015
%A Sahil Pathan
%A Karan Panjwani
%A Nitin Yadav
%A Shreyas Lokhande
%A Bhushan Thakare
%T Product Recommendations System Survey%T
%J International Journal of Computer Applications
%V 131
%N 9
%P 36-38
%R 10.5120/ijca2015907394
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Recommendation Systems are used to increase the growth of various online businesses. E-commerce players are utilizing such systems to get high sales. Such systems make use of statistics and data from user behaviour e.g. Purchase history, product ratings. So, decision to display a specific product from a specific category is taken after considering such parameters. In Hyper-Local based services (Locality Based) recommendation systems operate in a challenging environment. Such as, new customers have too much limited information associated, less purchase history, no product ratings etc. Secondly a large retailer have too much categories to choose from. Last, users tends have scattered data-less patterns. In order to handle such information mainly three methods are available: search-based methods, collaborative filtering and cluster models. These methods are more suitable in a vast user base environment. Whereas, in small scale environments a set of customers whose purchased and rated products overlaps with a current user's purchased and rated products are subject to a simple measurements.
References
- J.B. Schafer, J.A. Konstan, and J. Reidl, “E-Commerce Recommendation Applications,” Data Mining and Knowledge Discovery, Kluwer Academic, 2001, pp. 115-153.
- P. Resnick et al., “GroupLens: An Open Architecture for Collaborative Filtering of Netnews,” Proc. ACM 1994 Conf.Computer Supported Cooperative Work, ACM Press, 1994, pp. 175-186.
- B.M. Sarwarm et al., “Analysis of Recommendation Algorithms for E-Commerce,”ACM Conf. Electronic Commerce, ACM Press, 2000, pp.158-167.
- L. Ungar and D. Foster, “Clustering Methods for Collaborative Filtering,” Proc. Workshop on Recommendation Systems, AAAI Press, 1998.
- B.M. Sarwar et al., “Item-Based Collaborative FilteringRecommendation Algorithms,” 10th Int’l World Wide Web Conference, ACM Press, 2001, pp. 285-295.
- P.S. Bradley, U.M. Fayyad, and C. Reina, “Scaling Clustering Algorithms to Large Databases,” Knowledge Discovery and Data Mining, Kluwer Academic, 1998, pp. 9-15.
- L. Ungar and D. Foster, “Clustering Methods for Collaborative Filtering,” Proc. Workshop on Recommendation Systems, AAAI Press, 1998.
- M. Balabanovic and Y. Shoham, “Content-Based Collaborative Recommendation,” Comm. ACM, Mar. 1997, pp. 66-72.
- G.D. Linden, J.A. Jacobi, and E.A. Benson, Collaborative Recommendations Using Item-to- Item Similarity Mappings, US Patent 6,266,649 (to Amazon.com), Patent and Trademark Office, Washington, D.C., 2001.
- Canny, J. F. (2002). Collaborative filtering with privacy via factor analysis. SIGIR (pp. 238–245). ACM.
- Carreira-Perpinan, M., & Hinton, G. (2005). On contrastive divergence learning. 10th Int. Workshop on Artificial Intelligence and Statistics (AISTATS’2005).
Index Terms
Keywords