PNP: Fast Path Ensemble Method for Movie Design

TL;DR

This paper introduces PNP, a fast and scalable method for designing movies tailored to specific user groups by optimizing feature selection on a heterogeneous graph, outperforming existing approaches.

Contribution

The paper presents PNP, a novel tripartite graph-based optimization method for personalized movie design, demonstrating superior performance over state-of-the-art techniques.

Findings

PNP outperforms matrix factorization and other methods.

It effectively targets different user groups.

The approach is scalable to large datasets.

Abstract

How can we design a product or movie that will attract, for example, the interest of Pennsylvania adolescents or liberal newspaper critics? What should be the genre of that movie and who should be in the cast? In this work, we seek to identify how we can design new movies with features tailored to a specific user population. We formulate the movie design as an optimization problem over the inference of user-feature scores and selection of the features that maximize the number of attracted users. Our approach, PNP, is based on a heterogeneous, tripartite graph of users, movies and features (e.g., actors, directors, genres), where users rate movies and features contribute to movies. We learn the preferences by leveraging user similarities defined through different types of relations, and show that our method outperforms state-of-the-art approaches, including matrix factorization and other heterogeneous graph-based analysis. We evaluate PNP on publicly available real-world data and show that it is highly scalable and effectively provides movie designs oriented towards different groups of users, including men, women, and adolescents.