Shopbot 2.0: Integrating Recommendations and Promotions with Comparison Shopping

Recommender systems have been used by online retailers along with various promotions to attract customers. They are often in the form of a single item (best bet) along with a choice set. The majority of choice set recommendations are made based on collaborative filtering algorithms that recommend highly related items. However, we observe that very often best bets suggested by retailers are not based strictly on relatedness, since they are not members of the choice set. We found that the probability of this occurring is positively related to the popularity of the original requested item (base item). We also show that, even when best bets are closely related to base items, there are alternate options for the best bet that are still highly related, and at the same time can integrate with existing promotions to be more appealing to price sensitive customers. We argue that shopbots are in the best position to provide such integrated service and we therefore develop an integer programming model to optimize recommendations for shopbots. This model is validated using data from two online book retailers to show that significant extra savings can be achieved by suggesting alternate best bets.

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