Recmax: Exploiting recommender systems for fun and profit

In recent times, collaborative filtering based Recommender Systems (RS) have become extremely popular. While re-search in recommender systems has mostly focused on im-proving the accuracy of recommendations, in this paper, we look at the “flip ” side of a RS. That is, instead of improving existing recommender algorithms, we ask whether we can use an existing operational RS to launch a targeted mar-keting campaign. To this end, we propose a novel problem called Recmax that aims to select a set of “seed ” users for a marketing campaign for a new product, such that if they endorse the product by providing relatively high ratings, the number of other users to whom the product is recommended by the underlying RS algorithm is maximum. We moti-vate Recmax with real world applications. We show that seeding can make a substantial difference, if done carefully. We prove that Recmax is not only NP-hard to solve opti-mally, it is NP-hard to even approximate within any reason-able factor. Given this hardness, we explore several natural heuristics on 3 real world datasets – Movielens, Yahoo! Mu-sic and Jester Joke and report our findings. We show that even though Recmax is hard to approximate, simple nat-ural heuristics may provide impressive gains, for targeted marketing using RS