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Predicting Movie Taste
There is a fundamental tension between how movie critics conceive of their role and how their reviews are utilized by the movie-going public. Movie critics by and large see their job as educating the public as to what is a good movie and explaining what makes it good. In contrast, the public generally just wants a recommendation as to what they might like to watch.
Given this fundamental mismatch, the results of our study, Strikingly Low Agreement in the Appraisal of Motion Pictures by Pascal Wallisch and Jake Alden Whritner, that investigated the question whether movie critics are good predictors of individual movie liking, should not be surprising.
First, we found that individual movie taste was radically idiosyncratic. The average correlation was only 0.26 – in other words, one would predict an average disagreement of 1.25 stars, out of a rating scale from 0 to 4 stars – that’s a pretty strong disagreement (max RMSE possible is 1.7). Note that these are individuals who reported having seen *the same* movies.
Interestingly, whereas movie critics correlated more strongly with each other – at 0.39 – which had been reported previously, on average they are not significantly better than a randomly picked non-critic at predicting what a randomly picked person will like. This suggests that vaunted critics like the late Roger Ebert gain prominence not by the reliability of their predictions, but other factors such as the force of their writing.
What is the best way to get a good movie recommendation? In absence of all other information, information aggregators of non-critics such as the Internet Movie Database do well (r = 0.49), whereas aggregators of critics such as Rotten Tomatoes underperforms, relatively speaking (r = 0.33) – Rotten Tomatoes is better at predicting what a critic would like (r = 0.55), suggesting a fundamental disconnect between critics and non-critics.
Finally, as taste is so highly idiosyncratic, your best bet might be to find a “movie-twin” – someone who shares your taste, but has seen some movies that you have not. Alternatively, companies like Netflix are now employing a “taste cluster” approach, where each individual is assigned to the taste cluster their taste vector is closest to, and the predicted rating would be that of the cluster (as the cluster has presumably seen all movies, whereas individuals, even movie-twins will not). However, one cautionary note about this approach is that Netflix probably does not have the data it needs to pull this off, as ratings are provided in a self-selective fashion, i.e. over-weighing those that people feel most strongly about, potentially biasing the predictions.
The author of this article, Pascal Wallisch, is the clinical assistant professor of Psychology at New York University. His research interests are at the intersection of Psychology and Neuroscience, specifically Cognitive and Computational Neuroscience. His current work focuses on motion perception, autism and the appraisal of film.
Dr. Wallisch is the co-author of books including the critically-acclaimed MATLAB for Neuroscientists, the only complete study manual and teaching resource for MATLAB, the globally accepted standard for scientific computing, in the neurosciences and psychology. Recently he co-authored Neural Data Science: A Primer with MATLAB® and Python, providing a beginner’s introduction to the principles of computation and data analysis in neuroscience, using both Python and MATLAB, giving readers the ability to transcend platform tribalism and enable coding versatility.
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