Product recommendations using linear predictive modeling

Recommendation systems apply statistical and knowledge discovery techniques to the problem of making product recommendations and they are achieving widespread success in E-Commerce these days. A successful recommendation system fulfils several purposes and the choice of the methodology significantly influences the quality of recommendations and other aspects including scalability. As the volume of data in the e-commerce is growing massively, the system should also be able to address the need to provide the recommendations either by in-memory calculations or offline calculations, both demanding the high performance. For a large number of customers and products, the linear regression with a proper model selection can provide significantly better results and performance. Recommendations engines are increasingly becoming a popular choice for solving the problem of content discovery enabling the user to find personally relevant content that they might not have known was available. In this paper, we consider linear regression technique for analyzing large-scale dataset for the purpose of useful recommendations to e-commerce customers by offline calculations of model results.