Show Me the Money: Dynamic Recommendations for Revenue Maximization

Recommender Systems (RS) play a vital role in applications such as e-commerce and on-demand content streaming. Research on RS has mainly focused on the customer perspective, i.e., accurate prediction of user preferences and maximization of user utilities. As a result, most existing techniques are not explicitly built for revenue maximization, the primary business goal of enterprises. In this work, we explore and exploit a novel connection between RS and the profitability of a business. As recommendations can be seen as an information channel between a business and its customers, it is interesting and important to investigate how to make strategic dynamic recommendations leading to maximum possible revenue. To this end, we propose a novel \model that takes into account a variety of factors including prices, valuations, saturation effects, and competition amongst products. Under this model, we study the problem of finding revenue-maximizing recommendation strategies over a finite time horizon. We show that this problem is NP-hard, but approximation guarantees can be obtained for a slightly relaxed version, by establishing an elegant connection to matroid theory. Given the prohibitively high complexity of the approximation algorithm, we also design intelligent heuristics for the original problem. Finally, we conduct extensive experiments on two real and synthetic datasets and demonstrate the efficiency, scalability, and effectiveness our algorithms, and that they significantly outperform several intuitive baselines.Comment: Conference version published in PVLDB 7(14). To be presented in the VLDB Conference 2015, in Hawaii. This version gives a detailed submodularity proo

Searching the eBay Marketplace

This paper proposes a framework for demand estimation with data on bids, bidders' identities, and auction covariates from a sequence of eBay auctions. First the aspect of bidding in a marketplace environment is developed. Form the simple dynamic auction model with IPV and private bidding costs it follows that if participation is optimal the bidder searches with a "reservation bid" for low-price auctions. Extending results from the empirical auction literature and employing a similar two-stage procedure as has recently been used when estimating dynamic games it is shown that bidding costs are non-parametrically identified. The procedure is tried on a new data set. The median cost is estimated at less than 2% of transaction prices.