Will I "Really Like" this Movie?

Navigating Movie Website Ratings to Select More Enjoyable Movies

How Do You Know a Tarnished Penny Isn’t a Tarnished Quarter?

One of my first posts on this site was The Shiny Penny in which I espoused the virtues of older movies. I still believe that and yet here I am, almost eleven months later, wondering if my movie selection algorithm does a good enough job surfacing those “tarnished quarters”. A more accurate statement of the problem is that older movies generate less data for the movie websites I use in my algorithm which in turn creates fewer recommended movies.

Let me explain the issue by using a comparison of IMDB voting with my own ratings for each movie decade. Since I began developing my algorithm around 2010, I’m also going to use 2010 as the year that I began disciplining my movie choices to an algorithm. Also, you might recall from previous posts, that my database consists of movies I’ve watched in the last fifteen years. Each month I remove movies from the database that go beyond the fifteen years and make them available for me to watch again. One other clarification, I use the IMDB ratings for age 45+ to better match with my demographic.

To familiarize you with the format I’ll display for each decade here’s a look at the 2010’s:

Database Movies Released in the 2010’s # of Movies % of Movies Avg # of Voters Avg. IMDB Rating My Avg. Rating
Viewed After Algorithm 340 100.0%    10,369 7.3 7.3
Viewed Before Algorithm 0 0.0%

The 340 movies that I’ve seen from the 2010’s are 17.2% of all of the movies I’ve seen in the last 15 years and there are three more years in the decade to go. If the number of recommended movies were distributed evenly across all nine decades this percentage would be closer to 11%. Because the “shiny pennies” are the most available to watch, there is a tendency to watch more of the newer movies. I also believe that many of the newer movies fit the selection screen before the data matures that might not fit the screen after the data matures. The Average # of Voters column is an indicator of how mature the data is. Keep this in mind as we look at subsequent decades.

The 2000’s represent my least disciplined movie watching. 38.4% of all of the movies in the database come from this decade. The decision to watch specific movies was driven primarily by what was available rather than what was recommended.

Database Movies Released in the 2000’s # of Movies % of Movies Avg # of Voters Avg. IMDB Score Avg.My Score
Viewed After Algorithm 81 10.6%    10,763 7.2 6.8
Viewed Before Algorithm 680 89.4%    10,405 7.1 6.4

One thing to remember about movies in this decade is that only movies watched in 2000 and 2001 have dropped out of the database. As a result, only 10.6% of the movies were selected to watch with some version of the selection algorithm.

The next three decades represent the reliability peak in terms of the algorithm.

Database Movies Released in the 1990’s # of Movies % of Movies Avg # of Voters Avg. IMDB Score Avg.My Score
Viewed After Algorithm 115 46.7%    18,179 7.4 8.1
Viewed Before Algorithm 131 53.3%    11,557 7.2 7.0
Database Movies Released in the 1980’s # of Movies % of Movies Avg # of Voters Avg. IMDB Score Avg.My Score
Viewed After Algorithm 68 44.4%    14,025 7.5 7.6
Viewed Before Algorithm 85 55.6%    12,505 7.4 7.0
Database Movies Released in the 1970’s # of Movies % of Movies Avg # of Voters Avg. IMDB Score Avg.My Score
Viewed After Algorithm 38 38.0%    18,365 7.8 7.6
Viewed Before Algorithm 62 62.0%      9,846 7.5 6.5

Note that the average number of voters per movie is higher for these three decades than the movies released after 2000. Each decade there is a growing gap in the number of voters per movie that get recommended by the algorithm and those that are seen before using the algorithm. This may be indicative of the amount of data needed to produce a recommendation. You also see larger gaps in my enjoyment of the movies that use the disciplined movie selection process against those movies seen prior to the use of the algorithm. My theory is that younger movie viewers will only watch the classics and as a result they are the movies that generate sufficient data for the algorithm to be effective.

When we get to the four oldest decades in the database, it becomes clear that the number of movies with enough data to fit the algorithm is minimal.

Database Movies Released in the 1960’s # of Movies % of Movies Avg # of Voters Avg. IMDB Score Avg.My Score
Viewed After Algorithm 23 20.0%    14,597 8.0 8.3
Viewed Before Algorithm 92 80.0%      6,652 7.7 6.6
Database Movies Released in the 1950’s # of Movies % of Movies Avg # of Voters Avg. IMDB Score Avg.My Score
Viewed After Algorithm 22 18.0%    11,981 8.0 8.4
Viewed Before Algorithm 100 82.0%      5,995 7.7 5.9
Database Movies Released in the 1940’s # of Movies % of Movies Avg # of Voters Avg. IMDB Score Avg.My Score
Viewed After Algorithm 21 22.1%      8,021 8.0 7.9
Viewed Before Algorithm 74 77.9%      4,843 7.8 6.5
Database Movies Released in the Pre-1940’s # of Movies % of Movies Avg # of Voters Avg. IMDB Score Avg.My Score
Viewed After Algorithm 7 14.0%    12,169 8.0 7.5
Viewed Before Algorithm 43 86.0%      4,784 7.9 6.2

The results are even more stark. For these oldest decades of movies, today’s movie viewers and critics are drawn to the classics for these decades but probably not much else. It is clear that the selection algorithm is effective for movies with enough data. The problem is that the “really like” movies from these decades that don’t generate data don’t get recommended.

Finding tarnished quarters with a tool that requires data when data diminishes as movies age is a problem. Another observation is that the algorithm works best for the movies released from the 1970’s to the 1990’s probably because the data is mature and plentiful. Is there a value in letting the shiny pennies that look like quarters get a little tarnished before watching them?

Merry Christmas to all and may all of your movies seen this season be “really like” movies.

 

 

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