RecMax

Abstract

In recent times, collaborative filtering based Recommender Systems (RS) have become extremely popular. While research in recommender systems has mostly focused on improving 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 marketing 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 motivate 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 optimally, it is NP-hard to even approximate within any reasonable factor. Given this hardness, we explore several natural heuristics on 3 real world datasets - Movielens, Yahoo! Music and Jester Joke and report our findings. We show that even though RecMax is hard to approximate, simple natural heuristics may provide impressive gains, for targeted marketing using RS.