Extracting Features of Entertainment Products: A Guided LDA Approach Informed by the Psychology of Media Consumption

The authors propose a quantitative approach for describing entertainment products, in a way that allows improving the predictive performance of consumer choice models for these products. Their approach is based on the media psychology literature, which suggests that the consumption of entertainment products by individuals is influenced by the psychological themes featured in these products. They classify psychological themes based on the “Character Strengths” taxonomy from the positive psychology literature (Peterson and Seligman, 2004). They develop a natural language processing tool, Guided LDA, that automatically extracts a set of features of entertainment products based on their descriptions. Guided LDA is flexible enough to allow features to be informed by psychological themes, while allowing other relevant dimensions to emerge. They apply this tool to movies. They show that Guided LDA features help better predict movie-watching behavior at the individual level. They find this result both with award-winning movies and blockbuster movies. They illustrate the potential of the proposed approach in pure content-based predictive models of consumer behavior, as well as in hybrid predictive models that combine content-based models with collaborative filtering. They also show that Guided LDA can improve the performance of models that predict aggregate outcomes. ∗Glaubinger Professor of Business, Graduate School of Business, Columbia University, ot2107@gsb.columbia. edu. †Professor, Industrial Engineering and Operations Research Department, Columbia University, garud@ieor.columbia. edu. ‡Real.org; Real Engagement and Loyalty (REAL); OWEN.AI, rb@real.org §Graduate Student, Graduate School of Business, Columbia University, ALemaire18@gsb.columbia. edu. The revenue of the global entertainment and media industry was estimated at $1.8 trillion in 2016 (Statista, 2017b). One important trend in this industry is the increasing use of digital services such as streaming, video-on-demand, e-readers, etc. For example, the overthe-top streaming market (including Netflix, Hulu, etc.) in the US alone is expected to grow from $4.67B in 2013 to $12.64B in 2019 (Statista, 2016a), and it is predicted that 28% of the US population will own an e-Reader by 2020 (Statista, 2017a). Importantly for marketers, these technologies increase the availability of panel data in which consumers are observed making decisions over time. In addition, many brick-and-mortar distributors of entertainment products now offer loyalty programs to their customers (e.g., Regal Crown Club, AMC Stubs Card, Cinemarks’ CineMode for movies, B&N Membership for books, etc.), which provide panel data of a similar nature. Approaches for leveraging panel data in the media and entertainment industry have been classified into three categories (Adomavicius and Tuzhilin, 2005): pure collaborative approaches, where the behavior of a user is predicted based on the past behavior of similar users; content-based approaches, where the behavior of a user is predicted based on their own past behavior; and hybrid methods, which combine collaborative and content-based methods. Popular collaborative approaches include variants of neighborhood-based Collaborative Filtering (Breese et al., 1998; Linden et al., 2003) and latent factor models (Koren et al., 2009). Content-based approaches often use various types of regressions, decision trees, or neural networks to link product features to consumption. Popular hybrid approaches include Content-Boosted Collaborative Filtering (Melville et al., 2002),and the Bayesian approach proposed by Ansari et al. (2000). In the marketing literature, most consumer choice models for entertainment products have been content-based or hybrid (e.g., Ansari et al., 2000; Bo-