Last Saturday night my wife Pam and I watched 20th Century Woman for our weekend movie night. If you’ve been following the Objective Top Twenty, you’ll note that this movie has been on the list for most of the year. We were pretty excited to see it. In the end, though, it wasn’t the movie we expected.
20th Century Woman is a semi-autobiographical movie directed and written by Mike Mills and reminisces about his teenage years in Santa Barbara, CA, He is raised by a single mother, played by Annette Bening, with the assistance of two other women in his social circle.
It is an intriguing movie with interesting characters. I wasn’t bored by it but the movie didn’t quite connect with me. As an aside, I found it interesting that Greta Gerwig, who co-stars as one of the other female influences in the story, turned around after this movie and drew on her own teenage experience in Sacramento, CA. Gerwig wrote and directed a similar movie, the recently released and highly acclaimed Lady Bird. While Mills made the focus of his movie about the mother, Gerwig centered her movie on Lady Bird, the teenager. Perhaps 20th Century Woman would have more effectively connected with me if it were focused on the teenager, Jamie. Punk Rock also has a prominent place in 20th Century Woman, a music genre that passed me by without hardly an acknowledgement of its existence.
I ended up rating this movie as a “like” but not a “really like” movie. The “really like” algorithm estimated that there was a 67% probability that I would “really like” 20th Century Woman. Is this a case of the movie simply representing the 33% probability that I wouldn’t “really like” it. Sure, but that doesn’t mean that there weren’t warning signs that it might end up in the 33%.
Without getting into the mathematical weeds of the algorithm, let it suffice to say that the probability that I will “really like” a movie is the blend of the objective data that goes into the Objective Top Twenty and subjective data from Netflix, Movielens, and Criticker which are based on my personal taste in movies. If the data from the subjective sites is limited, my “really like” probability is weighted closely to the objective data. On the other hand, if the subjective data is plentiful, then its recommendation is very reliable and my “really like” probability is close to the subjective recommendation.
You might find this illustration helpful. The Credibility Quintile organizes the movies into five groups based on how reliable the subjective data is. Quintile 5 is very reliable data and Quintile 1 is not very reliable. The five movies listed all have close to the same probability that I will “really like” them but are in different quintiles.
|Movie||Credibility Quintile||Objective “Really Like” Probability %||Subjective “Really Like” Probability %||Probability I Will “Really Like” This Movie|
|Men of Honor||5||63.4%||69.0%||67.2%|
|Far and Away||4||61.6%||69.6%||66.6%|
|Fabulous Baker Boys, The||2||65.3%||69.9%||67.0%|
|20th Century Women||1||68.3%||51.2%||67.0%|
While all five movies have relatively the same overall probability, they aren’t equally reliable. Men of Honor is clearly a movie that, according to the highly reliable Quintile 1 data, I will like more than the rest of the world and the algorithm reflects that. The same could be said for Far and Away. The movie Nebraska, on the other hand, seems to be a movie that I would like less than the general public. Note as a Quintile 3 movie my probability is halfway between the objective and the subjective probabilities.
It’s the last two movies that illustrate the point I want to make. The probability that I will “really like” The Fabulous Baker Boys is identical to 20th Century Woman. Both movies are in below average credibility quintiles. That is where the similarities end. When you look at the subjective probabilities for both movies, The Fabulous Baker Boys has a strong trend towards being a movie I will “really like”. Even without reliable data it might be a movie worth taking a chance on. 20th Century Woman is headed in the opposite direction towards being a movie I probably wouldn’t “really like”. I should have caught that before watching the movie. It doesn’t mean I would have given up on the movie. It just means that I should have waited another cycle or two for more data to more reliably predict whether I would “really like” it or not.
Algorithms are tools to help you analyze data. Using algorithms to make decisions requires the exercise of a little judgement.