RecMax: Exploiting Recommender Systems for Fun and Profit

1. A B E C D • Key Theoretical Results • RecMax is NP-hard to solve exactly. • RecMax is NP-hard to approximate within a factor of 1/|V|1-ε for any ε > 0. • It is as hard as Maximum Independent Set Problem. • Idea behind reduction: In order to achieve hit score of 2, {B,C,E} must rate the new product. • Datasets • Heuristics • Random: Seed Set is Selected Randomly. • Most-Active: Select top-k users with most number of ratings. • Most-Positive: Select top-k user with most positive average ratings. • Most-Critical: Select top-k users with most critical average ratings. • Most-Central: Select top-k central users with highest aggregate similarity scores. • Experiments • RecMax – Recommendation Maximization • Previous research in Recommender Systems mostly focused on improving the accuracy of recommendations. • We propose a novel problem, RecMax,in this paper – Can we launch a targeted marketing campaign over an existing operational Recommender Systems (RS)? • “Select k users such that if they provide high ratings to a new product, then the number of other users to whom the product is recommended by the underlying recommender system (RS) algorithm is maximum.” • Benefits of RecMax • Targeted marketing in RS • Marketers can effectively advertise new products on a RS platform • Business opportunity to RS platform (service provider). • Beneficial to seed users • They get free/discounted samples of a new product • Helpful to other users • They receive recommendations of new products – solution to cold start problem • Problem Formulation • The single most important challenge is to study RecMax is the wide diversity of RS algorithms. • We focus on User-based (with Pearson Correlation as similarity function) and Item-based RS (with Adjusted Cosine similarity function). • Hit Score: • The goal of RecMax is to find a seed set S such that hit score f(S) is maximized. • Does Seeding Help? • Hit Rate achieved by random seed set on Movielens dataset on User-based (left) and Item-based (right). The plots show that even when the seed sets are selected randomly, seeding does help and exhibits impressive gains in hit score. Nodes {A,D} form Maximum Independent Set Nodes {B,C,E} encircle {A,D} (Maximum Encirclement Problem) RecMax: Exploiting Recommender Systems for Fun and Profit Recommendation List for user v • Hit Score Variation on User-based: • Follows S-curve. • Most-Central and Most-Positive perform good. Most Central wins overall. • Most-Active and Most-Critical perform poorly, on all datasets. rating threshold of user v (denoted by θv) l recommendations • Hit Score Variation on Item-based: • Tipping point is achieved very early. • Less seeding is required for converge. • Most-Central performs better overall. If expected rating R(v,i) >θv, then the new item is recommended to v • RecMax on User-based vs Item-based • Initial rise is much steeper in Item-based. • Eventual gain is much higher in User-based. • Out of 1000, number of common seeds are 103, 219 and 62 on 3 datasets – Seed sets are different for different RS. • Conclusion and Future Work • We propose a new problem RecMax. It has real world applications. We show that RecMax makes marketing sense, even if it is NP- hard to approximate. • Developing more effective heuristics is interesting and challenging. • Study of RecMax on more sophisticated recommender systems – like Matrix Factorization would be exciting. Paper ID: 727