Will I "Really Like" this Movie?

Navigating Movie Website Ratings to Select More Enjoyable Movies

Archive for the tag “MovieLens”

This One Is All About You and Movielens

A few months ago my daughter texted me for recommendations for good movies on Netflix or Amazon Prime. I recommended a hidden treasure of a movie, Begin Again, but I couldn’t remember if it was on Netflix or Amazon. I knew it was on one of them. I had to go to each site to find the movie to nail down which streaming service it was on.

My daughter, and others like her, will no longer need to search blindly for movies on the streaming services they subscribe to if they’ve signed up to use my favorite movie recommender site, Movielens. Aside from being a very reliable movie recommender site, it is also the most useful in terms of finding movies to watch.

Within the last couple of months Movielens has added its best feature to date. Not only can you get pages and pages of recommended movies, once you’ve taken the time to rate the movies you’ve seen, but now you can filter them by the most popular streaming services.

Movielens allows you to filter recommendations by movies currently on Netflix, Amazon, Hulu, HBO, and Showtime. You can filter them individually or in combination. In my case, I filter by Netflix, Amazon and HBO. This means that you can get a list of movies that you can watch right now, ranked by the probability that you will “really like” them.

If I go to the Home Page of Movielens right now and go to Top Picks, I can click on the filter’s drop down menu and select Streaming Services. This will provide me with a list of the five services mentioned previously. By clicking on Netflix, Amazon, and HBO, I get a list of movies that I can watch now that I haven’t seen before. There are 5,256 movies available for me to watch right now ranked from the one I’m most likely to enjoy, last summer’s box office surprise Me Before You (Amazon), to the movie I’m least likely to enjoy, The Admirer (Amazon). You’ve never heard of The Admirer? Neither have I. It is a 2012 Russian movie based on the love between Anton Chekhov and a young writer, Lidiya Avilova. ZZZ.

More often than not my posts are about my experiences in finding movies that I will “really like”. This one’s for you. If you only have time to track one movie recommender website, go to Movielens. It will be fun and it will save you time scrolling through lines and lines of movies searching for movies that you might like.


When It Comes to Unique Movies, Don’t Put All of Your Movie Eggs in the Netflix Basket.

It is rare to find a movie that isn’t a sequel, or a remake, or a borrowed plot idea, or a tried and true formula. Many of these movies are entertaining because they feel familiar and remind us of another pleasant movie experience. The movie recommender websites Netflix, Movielens, and Criticker do a terrific job of identifying those movie types that you “really liked” before and surfacing those movies that match the familiar plot lines you’ve enjoyed in the past.

But, what about those movies that are truly original. What about those movies that present ideas and plot lines that aren’t in your usual comfort zone. Will these reliable websites surface these unique movies that I might like? Netflix has 20,000+ genre categories that they slot movies into. But, what if a movie doesn’t fit neatly into one of those 20,000 categories.

Yesterday I watched a great movie, Being There.

Being There

This 1979 movie, starring Peter Sellers in an Academy Award nominated performance, is about a simple-minded gardener who never left the home of his employer over the first fifty years of his life. Aside from gardening, the only knowledge he has is what he’s seen on television. After his employer dies he is forced to leave his home. The movie follows Chance the Gardener as he becomes Chauncey Gardner, economic advisor to the President. It’s not a story of transformation but of perception. The movie is fresh.

My most reliable movie recommenders, Netflix and Movielens, warned me away from this movie. The only reason I added it to my weekly Watch List is because I saw the movie in the theater when it first came out in 1979 and “really liked” it.

Another possible reason why Netflix missed on this movie is because I hated Peter Sellers’ other classic movie Dr. Strangelove. I rated it 1 out of 5 stars.  If Being There is slotted among a Netflix category of Peter Sellers classics, it might explain the mismatch.

Is it impossible, then, to surface movies that have creative original content that you might like. Not entirely. Criticker predicted I would rate Being There an 86 out of 100. I gave it an 89. The IMDB rating is 8.0 based on over 54,000 votes. Rotten Tomatoes has it at 96% Certified Fresh. This is why I incorporate ratings from five different websites into the “really like” model rather than just Netflix.

When it comes to unique movies, don’t put all of your movie eggs in the Netflix basket.



Playing Tag with Movielens, Redux

Last July I wrote an article introducing my use of tags in Movielens to organize my movies. I can’t impress upon you enough how useful this tool is to someone as manic about movies as I am.

Regular readers of this blog know that I’ve shifted my focus to Oscar nominated movies. My research revealed that movies that haven’t receive a single Academy Award nomination have only a 35.8% chance of being a “really like” movie. On the other hand, even a single minor nomination increases the “really like” odds to around 55%. My algorithm now incorporates Oscar recognition.

Based on this finding, I’ve altered my tagging strategy. I created an “Oscar” tag that I attach to any movie I run across that has received even a single nomination. Many of these movies are older without enough credible data in the movie ratings websites to earn reliable recommendations. The probabilities in my algorithm for these Quintile 1 & 2 movies are driven by their Oscar performance.

Movies that pique my interest that weren’t Oscar nominated are tagged separately. Now, because these movies have no Oscar nominations, a Quintile 1 or 2 movie is going to have a “really like” probability closer to the 35% mark that reflects its “no nomination” status. It can only climb to a high enough probability to be considered for my weekly watch list if it is highly recommended by the movie websites and it falls into a high enough credibility quintile that its Oscar status doesn’t matter much.

I apply one of two tags to non-Oscar nominated movies. If they have fewer than 25 ratings in Movielens, I tag them as “might like”. Realistically, they have no chance of being highly recommended in my algorithm until the number of ratings received from Movielens raters becomes more robust.

Those non-Oscar nominated movies that have more than 25 ratings are tagged as “prospect”. Movies with the “prospect” tag that are highly rated by the websites and have enough ratings to reach higher credibility quintiles can reach a “really like” probability high enough to be considered for the watch list. For example, a quintile 5 movie like The American President can earn a 75% “really like” probability even though it was never nominated for an Academy Award.

I also have created tags for movies I don’t want to see even though they are highly rated. If I’ve already seen a movie and I don’t want to see it again, I tag it “not again”. If I’ve never seen a movie but it’s just not for me, I tag it “not interested”. Movielens also has the capability of hiding movies that you don’t want to see in any of your searches for movies. I take advantage of this feature to hide my “not again” and “not interested” tagged movies.

So, I’ve tagged all of these movies. Now what do I do with them. That will be included in next week’s topic “Building a Watch List”.

The Art of Selecting “Really Like” Movies: New Movies

I watch a lot of movies, a fact that my wife, and occasionally my children, like to remind of. Unlike the average, non-geeky, movie fan, though, I am constantly analyzing the process I go through to determine which movies I watch. I don’t like to watch mediocre, or worse, movies. I’ve pretty much eliminated bad movies from my selections. But, every now and then a movie I “like” rather than “really like” will get past my screen.

Over the next three weeks I’ll outline the steps I’m taking this year to improve my “really like” movie odds. Starting this week with New Movies, I’ll lay out a focused strategy for three different types of movie selection decisions.

The most challenging “really like” movie decision I make is which movies that I’ve never seen before are likely to be “really like” movies. There is only a 39.3% chance that watching a movie I’ve never seen before will result in a “really like” experience. My goal is to improve those odds by the end of the year.

The first step I’ve taken is to separate movies I’ve seen before from movies I’ve never seen in establishing my “really like” probabilities. As a frame of reference, there is a 79.5% chance that I will “really like” a movie I’ve seen before. By setting my probabilities for movies I’ve never seen off of the 39.3% probability I have created a tighter screen for those movies. This should result in me watching fewer never-before-seen movies then I’ve typically watched in previous years. Of the 20 movies I’ve watched so far this year, only two were never-before-seen movies.

The challenge in selecting never-before-seen movies is that, because I’ve watched close to 2,000 movies over the last 15 years, I’ve already watched the “cream of the crop” from those 15 years.. From 2006 to 2015, there were 331 movies that I rated as “really like” movies, that is 33 movies a year, or less than 3 a month. Last year I watched 109 movies that I had never seen before. So, except for the 33 new movies that came out last year that, statistically, might be “really like” movies, I watched 76 movies that didn’t have a great chance of being “really like” movies.

Logically, the probability of selecting a “really like” movie that I’ve never seen before should be highest for new releases. I just haven’t seen that many of them. I’ve only seen 6 movies that were released in the last six months and I “really liked” 5 of them. If, on average, there are 33 “really like” movies released each year, then, statistically, there should be a dozen “really like” movies released in the last six months that I haven’t seen yet. I just have to discover them. Here is my list of the top ten new movie prospects that I haven’t seen yet.

My Top Ten New Movie Prospects 
New Movies =  < Release Date + 6 Months
Movie Title Release Date Last Data Update “Really Like” Probability
Hacksaw Ridge 11/4/2016 2/4/2017 94.9%
Arrival 11/11/2016 2/4/2017 94.9%
Doctor Strange 11/4/2016 2/6/2017 78.9%
Hidden Figures 1/6/2017 2/4/2017 78.7%
Beatles, The: Eight Days a Week 9/16/2016 2/4/2017 78.7%
13th 10/7/2016 2/4/2017 78.7%
Before the Flood 10/30/2016 2/4/2017 51.7%
Fantastic Beasts and Where to Find Them 11/18/2016 2/4/2017 51.7%
Moana 11/23/2016 2/4/2017 51.7%
Deepwater Horizon 9/30/2016 2/4/2017 45.4%
Fences 12/25/2016 2/4/2017 45.4%

Based on my own experience, I believe you can identify most of the new movies that will be “really like” movies within 6 months of their release, which is how I’ve defined “new” for this list. I’m going to test this theory this year.

In case you are interested, here is the ratings data driving the probabilities.

My Top Ten New Movie Prospects 
Movie Site Ratings Breakdown
Ratings *
Movie Title # of Ratings All Sites Age 45+ IMDB Rotten Tomatoes ** Criticker Movielens Netflix
Hacksaw Ridge         9,543 8.2 CF 86% 8.3 8.3 8.6
Arrival      24,048 7.7 CF 94% 8.8 8.1 9.0
Doctor Strange      16,844 7.7 CF 90% 8.2 8.3 7.8
Hidden Figures         7,258 8.2 CF 92% 7.7 7.3 8.2
Beatles, The: Eight Days a Week         1,689 8.2 CF 95% 8.0 7.3 8.0
13th    295,462 8.1 CF 97% 8.3 7.5 8.0
Before the Flood         1,073 7.8 F 70% 7.6 8.2 7.8
Fantastic Beasts and Where to Find Them      14,307 7.5 CF 73% 7.3 6.9 7.6
Moana         5,967 7.7 CF 95% 8.4 8.0 7.0
Deepwater Horizon      40,866 7.1 CF 83% 7.8 7.6 7.6
Fences         4,418 7.6 CF 95% 7.7 7.1 7.2
*All Ratings Except Rotten Tomatoes Calibrated to a 10.0 Scale
** CF = Certified Fresh, F = Fresh

Two movies, Hacksaw Ridge and Arrival, are already probably “really like” movies and should be selected to watch when available. The # of Ratings All Sites is a key column. The ratings for Movielens and Netflix need ratings volume before they can credibly reach their true level. Until, there is a credible amount of data the rating you get is closer to what an average movie would get. A movie like Fences, at 4,418 ratings, hasn’t reached the critical mass needed to migrate to the higher ratings I would expect that movie to reach. Deep Water Horizon, on the other hand, with 40,866 ratings, has reached a fairly credible level and may not improve upon its current probability.

I’m replacing my monthly forecast on the sidebar of this website with the top ten new movie prospects exhibit displayed above. I think it is a better reflection of the movies that have the best chance of being “really like” movies. Feel free to share any comments you might have.


In Pursuit of “Really Like” Movies, Playing Tag with Movielens Can Be Helpful

You may wonder where my list of Top Ten Movies Available To Watch This Week comes from. Or, you may be curious how far Mad Movie Man madness extends. Or, maybe you neither wonder nor are curious, but are instead just happy to have another place to go to for movie recommendations. Whether you have questions unanswered or questions unasked, today is your lucky day. This is the day that you discover how obsessive I can be in pursuit of movies that I will “really like”.

Before I plumb the depths of this madness, a few words about a Movielens tool that helps me organize the mania, would be appropriate. Movielens has a tagging system. There are Community Tags that are available for everyone to use. For example, here are the Community Tags for There’s Something About Mary.


You can use these tags as an additional screen when searching for a movie to watch. The number next to the tag tells you how often the tag has been used for this movie. By looking at these tags you could conclude that this movie isn’t for everyone and it probably wouldn’t meet the cringeworthy test. There is a plus sign next to the tag that allows you to agree or disagree with the tag. Also, if you thought the movie was “hilarious”, you can click on the tag and all of the movies will come up that have been tagged “hilarious” Try it on the list above.

If you want to keep track of all of the movies that you thought were “hilarious” there is a box where you can make “hilarious” one of your own personal tags for this movie. If you want to add a tag that isn’t listed for this movie, you can do that too. It is a very dynamic system for gaining additional insights into a movie and for helping you to organize your movies if you are so inclined.

Which brings me back to my manic inclinations. I keep a list of approximately 450 movies for which I’ve calculated “really like” probabilities. Each of these movies I assign the Movielens tag “reviewed” to. This allows me to keep track of movies that I’ve already put on my list of 450. These movies stay on the list as long as they qualify as a recommended movie on one of the five movie websites I use. The data for each movie is refreshed every 90 days.

Movies that I come across that aren’t on the list of 450 that I’m intrigued by are tagged “prospect” in Movielens. Whenever I watch a movie from the list of 450, or it is removed from the list because one of the websites no longer recommends it, I replace it with a movie from the “prospect” list. Movielens allows you to go to Your Tags and sort the movies you’ve tagged. For example, I take the movies that I’ve tagged “prospect” and Movielens sorts them by Movielens Recommended movie, from the highest to the lowest. The highest recommended “prospect” movie moves to the list of 450 to replace the movie removed from the list.

Each Wednesday, after reviewing which movies from the list of 450 are available to watch on the various movie outlets available to me, I rank them by their “really like” probabilities, with the top ten making my list.

Now you understand why I’m the “Mad” Movie Man and how playing tag with Movielens enables my madness.




Until That Next Special Movie Comes Along

Happy 4th of July to all of my visitors from the States and, to my friends to the North, Happy Canada Day which was celebrated on this past Saturday. It is a good day to watch Yankee Doodle Dandy, one of those special movie experiences I’m fond of.

This past weekend I watched another patriotic movie,  Courage Under Fire with Denzel Washington, Meg Ryan, and a young Matt Damon among others in a terrific cast. It was one of those special movies that I yearned for in my last post on July movie prospects. It was a July 1996 release that wasn’t nominated for an Academy Award (how it didn’t get an acting nomination among several powerful performances astounds me). It earned a 94 out of 100 score from me. I loved this movie. The feeling I get after watching a movie this good is why I watch so many movies. It is the promise that there are more movies out there to see that I will love that feeds my passion for movies.

As I was thinking about special movies the last few days, a question occurred to me. Can I use my rating system to find movies I’ll “love” rather than just “really like”? Of course I can. Any movie that earns a rating of 85 out of 100 or higher meets my definition of a movie I will “love”. An 85 also converts to a five star movie on Netflix. I can rank each of the movie rating websites that I use in my algorithm from highest rating to lowest. I then can take the top 10% of the rankings and calculate the probability that a movie in that top 10% would earn a score of 85 or higher. Regular readers of this blog shouldn’t be surprised by the results.

Top 10% Threshold Actual % of My Database Probability for “Love” Movie
Netflix >  4.5 9.5% 81.4%
Movielens >  4.2 10.7% 76.9%
Criticker >  90 10.3% 55.4%
IMDB >  8.1 10.8% 45.8%
Rotten Tomatoes >  Cert. Fresh 95% 10.4% 41.7%

High Netflix and Movielens scores are the most reliable indicators of “love” movies. Here’s my problem. There are no movies that I haven’t seen in the last fifteen years that have a Netflix Best Guess of 4.5 or higher. There are fewer than 10 movies that I haven’t seen in the last fifteen years with a Movielens predicted score of greater than 4.2. Here’s the kicker, the probability that I will “love” a movie with a Movielens predicted score of 4.2 or better that doesn’t also have a Netflix Best Guess greater than 4.5 is only 62%. It seems the chances to find movies to “love” are significantly diminished without the strong support of Netflix.

On the 1st of each month Netflix Streaming and Amazon Prime shake up the movies that are available in their inventory. The July 1 shakeup has resulted in a couple of new movies being added to my list of the Top Ten “Really Like” Movies Available on Netflix or Amazon Prime. This list is actually mistitled. It should be the Top Ten “Love” Movies Available. Take a look at the list. Perhaps you haven’t seen one of these movies, or haven’t seen it in a while. It is your good fortune to be able to watch one of these movies the next time you are in the mood for a special movie experience.

As for me, I’m still hoping that one of the movies released this year rises to the top of my watch list and is able to captivate me. If it were easy to find movies that I will “love”, I would have named this blog Will I “Love” This Movie?. For now, I will continue to watch movies that I will “really like” until that next special movie comes along.

In the Battle of Memory vs. Movie Website, Netflix is Still the Champ

On Monday I posed the question, is your memory of a movie that you’ve already seen the best predictor of “really like” movies. Based on Monday’s analysis memory certainly comes out on top against IMDB and Rotten Tomatoes. Today, I’m extending the analysis to Criticker, Movielens, and Netflix. By reconfiguring the data used in Monday’s post, you also can measure the relative effectiveness of each site. For example, let’s look again at IMDB.

Probability I Will “Really Like” Based on IMDB Recommendation
Recommended Not Recommended Percentage Point Spread
Seen Before 80.1% 69.2%                            0.11
Never Seen Before 50.6% 33.6%                            0.17

It’s not surprising that the probabilities are higher for the movies that were seen before. After all it wouldn’t make sense to watch again the movies you wished you hadn’t seen the first time. But by looking at the gap between the probability of a recommended movie and a non-recommended movie, you begin to see how effectively the movie recommender is at sorting high probability movies from low probability movies. In this instance, the small 11 point spread for Seen Before movies suggests that IMDB is only sorting these movies into small departures from average. The low probabilities for the Never Seen Before movies suggest that, without the benefit of the memory of a movie seen before, IMDB doesn’t do a very good job of identifying “really like” movies.

Rotten Tomatoes follows a similar pattern.

Probability I Will “Really Like” Based on Rotten Tomatoes Recommendation
Recommended Not Recommended Percentage Point Spread
Seen Before 80.5% 65.1%                            0.15
Never Seen Before 49.8% 31.8%                            0.18

Rotten Tomatoes is a little better than IMDB at sorting movies. The point spreads are a little broader. But, like IMDB, Rotten Tomatoes doesn’t effectively identify “really like” movies for the Never Seen Before group.

Theoretically, when we look at the same data for the remaining three sites, the Percentage Point Spread should be broader to reflect the more personalized nature of the ratings. Certainly, that is the case with Criticker.

Probability I Will “Really Like” Based on Criticker Recommendation
Recommended Not Recommended Percentage Point Spread
Seen Before 79.3% 56.4%                            0.23
Never Seen Before 45.3% 18.9%                            0.26

Like IMDB and Rotten Tomatoes, though, Criticker isn’t very effective at identifying “really like” movies for those movies in the Never Seen Before group.

When you review the results for Movielens, you can begin to see why I’m so high on it as a movie recommender.

Probability I Will “Really Like” Based on Movielens Recommendation
Recommended Not Recommended Percentage Point Spread
Seen Before 86.6% 59.6%                            0.27
Never Seen Before 65.1% 22.3%                            0.43

Unlike the three sites we’ve looked at so far, Movielens is a good predictor of “really like” movies for Never Seen Before movies. And, the spread of 43 points for the Never Seen Before movies is dramatically better than the three previous sites. It is a very effective sorter of movies.

Last, but certainly not least, here are the results for Netflix.

Probability I Will “Really Like” Based on Netflix Recommendation
Recommended Not Recommended Percentage Point Spread
Seen Before 89.8% 45.7%                            0.44
Never Seen Before 65.7% 21.4%                            0.44

What jumps off the page is that there is no memory advantage in the allocation of movies for Netflix. As expected, the Seen Before probabilities are higher. But, there is an identical 44 point gap for Seen Before movies and movies Never Seen Before. It is the only site where you have a less than 50% chance that you will “really like” a movie you’ve already seen if Netflix doesn’t recommend it.

“If memory serves me correctly, I “really liked” this movie the first time I saw it.” That is an instinct worth following even if the movie websites suggest otherwise. But, if Netflix doesn’t recommend it, you might think twice.


6/24/2016 Addendum

I’ve finalized my forecast for the last three movies on my June Prospect list. My optimism is turning to pessimism regarding my hopes that Independence Day: Resurgence and Free State of Jones would be “really like movies”. Unfavorable reviews from the critics and less than enthusiastic response from audiences suggest that they could be disappointments. Of my five June prospects, Finding Dory seems to be the only safe bet for theater viewing, with Me Before You a possibility for female moviegoers. The IMDB gender split is pronounced for Me Before You with female voters giving it an 8.1 rating and males a 7.3 rating. It is also one of those rare movies with more female IMDB voters than males.

Will You “Really Like” This Movie

My vision for this blog has never been to recommend movies for you to watch. Instead, my focus has been to familiarize you with tools on the internet that will help you find movies that you will “really like”. I also realize that not everyone has the time to rate movies to generate personalized recommendations. So, each week I’ve been posting two lists of movies that I will “really like”, not so much as recommendations, but as ideas for movies you might look into. As I’ve generated these lists, however, I’ve discovered that finding movies with a high probability that I will “really like”, after already watching 1,981movies, can be problematic. Worse still, many of the movies that I’m suggesting you look into don’t even have a high probability that I will “really like” them.

With yesterday’s post I’ve substituted a new list for My Top Ten Movies to Watch, which was really a misnomer. It really was my top ten movies not including the 1,981 movies I’d already watched. The new list, that I call the Top Ten Movies You Will “Really Like” Available on Netflix and Amazon Prime, includes all of the movies I’ve already seen plus the movies I haven’t seen.

Where do these movies come from? They are movies that are recommended by all five of the websites that I use. They are recommended by IMDB and Rotten Tomatoes which are not influenced by my ratings. They are also influenced by the three sites that are driven by my tastes – Netflix, Movielens, and Criticker. When all five sites recommend a movie there is a 74% probability that I will “really like” it. Just to provide some perspective, the sites that you are most likely to use if you don’t have time to do your own ratings are IMDB and Rotten Tomatoes. If IMDB has a 7.4 or higher average rating for a movie, there is a 55.9% chance I will “really like” it. If a movie is Certified Fresh by Rotten Tomatoes, it has a 60.4% probability I will “really like” it.

Of  the 1,981 movies I’ve seen over the last 15 years, 438 have been recommended by all five sites. Of those 438, I “really liked” 362 of them, or 82.6% of them. That’s a high percentage for a relatively large sample size. These movies are your best bets. There are only 8 movies I haven’t seen in the last 15 years that meet the 74% criteria.

I’ve only posted 10 of the 446 movies that are universally recommended by all five sites. Most of those ten you have probably already seen, but they might be worth another look if you haven’t seen them in a while. They are also available to watch now if you are a Netflix and Amazon Prime subscriber. I want to help you find movies that you will “really like”, even if you don’t have the time to rate your own movies.

Movielens Knows About Your Ratings

In the connected world that we live in, everyone knows us. The NSA, TSA, political campaigns, and Netflix are among the organizations who have profiles on us based on information we’ve supplied on the internet. If you have doubts about this there is a website called AboutTheData.com that will provide you with the data that has been collected on you for the use of various marketing organizations. In the age of Big Data we have sacrificed privacy for the convenience internet access provides us.

Movielens also has a profile on you based on the movie ratings you have provided the website. Unlike so many of the Big Data organizations out there, Movielens shares with you what they know. When you open their website there is a drop down menu right next to their logo. ( MovieLens logo )   The second column in the menu is titled YOUR ACTIVITY. Click on the link titled ABOUT YOUR RATINGS and a page full of graphs and information is provided to you that will offer you insights into your taste in movies.

A review of my ABOUT YOUR RATINGS supplies the following information:

  • December 22, 2006 was the first day I entered movie ratings. I rated 49 movies that day. On May 13, 2016 I rated my 2,009th movie.
  • My most common movie rating is a 4.0 (a “really like” rating) which I’ve given to 494 out of the 2,009 rated movies.
  • 433 of my rated movies were released between 2005 and 20015.
  • Drama is my most watched and favorite genre with 1,371 movies and an average rating of 3.71. The next closest genres were Comedy, 588 movies with 3.48 avg. rating, and Romance, 580 movies with 3.61 average rating. Of genres with at least 100 rated movies, Comedy is my lowest average rated.

ABOUT YOUR RATINGS also provides you with a list of movies that you dislike the most when compared to the average rating. These are my candidates for Razzies (the awards for the worst movies presented the day before the Oscars):

  • Requiem for a Dream (2000)
  • Dancer in the Dark (2000)
  • The Browning Version (1951)
  • Twin Peaks: Fire Walk with Me (1992)
  • There Will be Blood (2007)
  • The Other Side of the Mountain (2001)
  • The Magnificent Ambersons (1942)
  • How Green was my Valley (1941)

My unusual likes (or guilty pleasures) when compared to the average rating are:

  • Saturday Night Fever (1977)
  • The Karate Kid (1984)
  • Titanic (1997)
  • Pretty Woman (1990)
  • Ghost (1990)
  • Notting Hill (1999)
  • Somethings Gotta Give (2003)
  • A League of their Own (1992)

Now you know a lot about my taste in movies. Start rating movies in Movielens and you can discover a great deal about your taste in movies as well.

In the age of Big Data it is refreshing to find an organization with the transparency of Movielens.


Rating Movies: If You Put Garbage In, You’ll get Garbage Out

In my prior life, I would on occasion find myself leading a training session on the predictive model that we were using in our business. Since the purpose of the model was to help our Account Executives make more effective business decisions, one of the points of emphasis was to point out instances when the model would present them with misleading information that could result in ineffective business decisions. One of the most basic of these predictive model traps is that it relies on data input that accurately reflects the conditions being tested in the model. If you put garbage into the model, you will get garbage out of the model.

Netflix, MovieLens, and Criticker are predictive models. They predict movies that you might like based on your rating of the movies you have seen. Just like the predictive model discussed above, if the ratings that you input into these movie models are inconsistent from movie to movie, you increase the chances that the movie website will recommend to you movies that you won’t like. Having a consistent standard for rating movies is a must.

The best approach to rating movies is a simple approach. I start with the Netflix guidelines to rating a movie:

  • 5 Stars = I loved this movie.
  • 4 Stars = I really liked this movie.
  • 3 Stars = I liked this movie.
  • 2 Stars = I didn’t like this movie.
  • 1 Star = I hated this movie.

When I’ve used this standard to guide others in rating movies, the feedback has been that it is an easily understood standard. The primary complaint has been that sometimes the rater can’t decide between the higher and lower rating. The movie fits somewhere in between. For example, “I can’t decide whether I “really like” this movie or just “like” it. This happens enough that I’ve concluded that a 10 point scale is best:

  • 10 = I loved this movie.
  • 9 = I can’t decide between “really liked” and “loved”.
  • 8 = I really liked this movie.
  • 7 = I can’t decide between “liked” and “really liked”.
  • 6 = I liked this movie.
  • 5 = I can’t decide between “didn’t like” and “liked”.
  • 4 = I didn’t like this movie.
  • 3 = I can’t decide between “hated” and “didn’t like”.
  • 2= I hated this movie.
  • 1 = My feeling for this movie is beyond hate.

The nice thing about a 10 point scale is that it is easy to convert to other standards. Using the scales that exist for each of the websites, an example of the conversion would look like this:

  • IMDB = 7  (IMDB uses a 10 point scale already)
  • Netflix = 7 /2 = 3.5 = 4 rounded up.  (Netflix uses 5 star scale with no 1/2 stars)
  • Criticker = 7 x 10 = 70 (Criticker uses 100 point scale).
  • MovieLens = 7 /2 = 3.5 (MovieLens has a 5 star scale but allows input of 1/2 star)

Criticker, being on a 100 point scale, gives you the capability to fine tune your ratings even more. I think it is difficult to subjectively differentiate, for example, between an 82 and an 83. In a future post we can explore this issue further.

So from one simple evaluation of a movie you can generate a consistent rating across all of the websites that you might use. This consistency allows for a more “apples to apples” comparison.

So throw out the garbage. Good data in will produce good data out, and a more reliable list of movies that you will “really like”.


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