Shopbot 2.0: Integrating recommendations and promotions with comparison shopping

The current generation of shopbots reduce consumer search costs associated with determining the best purchase price and place to buy a product predetermined by the shopper. In order to provide better service to shoppers, the service horizon of these shopbots can be extended in several dimensions. In this paper, we suggest that shopbots can integrate retail promotions and incorporate recommender systems in order to provide greater values to their users. Although the majority of online retailers already provide recommender systems, we show that profit maximizing retailers may not always provide transparent recommendations and argue that shopbots are in the better position to offer such recommendations. We develop integer programming models for shopbots to integrate sales promotions and product recommendations. We validate our model by using product recommendation data from two popular online retailers, Amazon.com and Buy.com, to show that our model provides recommendations that offer better value to the price sensitive shopbot customers.