Analysis of bipartite network for new product recommendation

Availability of product consumption history of users make a challenge to extract users’ preference towards products and recommend product to the potential consumers. A customer-product-attribute relation can be represented using network. When a new product arrives the attributes will be clubbed to identify the customers who prefer those attributes and recommend the new product to them. This paper is primarily concerned about network representation of the data and extracts meaningful information which helps in recommendation of a new product. The empirical study shows significant improvement over few previous works.

[1]  Liang Hu,et al.  A reconsideration of negative ratings for network-based recommendation , 2018 .

[2]  Zhongfu Wu,et al.  Userrank for item-based collaborative filtering recommendation , 2011, Inf. Process. Lett..

[3]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[4]  Wesley W. Chu,et al.  A Social Network-Based Recommender System (SNRS) , 2010, Data Mining for Social Network Data.

[5]  Franz J. Király,et al.  The algebraic combinatorial approach for low-rank matrix completion , 2012, J. Mach. Learn. Res..

[6]  Yi-Cheng Zhang,et al.  Bipartite network projection and personal recommendation. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Fei Yu,et al.  Network-based recommendation algorithms: A review , 2015, Physica A: Statistical Mechanics and its Applications.

[8]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[9]  John Riedl,et al.  E-Commerce Recommendation Applications , 2004, Data Mining and Knowledge Discovery.