Weather Effect for Movie/Series Recommendations
When we are building a recommendation engine for movies and series, there was another very important metric rather than genre: Weather
We were trying to get a better recommendation engine for one of the IPTV/OTT companies that I was working for. We have already had one but it was so static and using static dimensions like movie/series genre, category, actors, actresses, etc. It was an OK-ish recommendation engine but I was aware that it was not enough to check these. Of course, if you are a crime-series-lover, you would like to see related ones but we need to think recommendation engine as a bucket. We should not fill 100% of the bucket with static recommendations.
I began to track myself. Nowadays, it is so popular like having a diary which also tracks your daily mood but in the experiment time, people were looking at me like I was an alien coming from Mars. I tracked my mood and what I am watching and listening to. There was no so big difference between music and movies/series so I thought that “Hmmm, let’s categorize it as media consumption”.
It was a bulb moment… When I searched the internet more on this, I saw that there is a theory: Mood Management Theory. I read everything that I could find about mood management theory and I realized that weather is one part but there are also a lot of variables: childhood, environment, friends, blood type for some crazy guys, etc. And I saw a lot of articles like this — I was not the only crazy guy to think about it. We have some data (at that time, data ownership was nothing) like gender, birth date, country but it is hard to understand what s/he talked to her/his friend, what kind of environment s/he raised definitely. So I tried to find some variables that I can use 80–90% quality and came with a solution of weather data which may never be 100% true and can change every hour but the quality is still good if you check regularly.
It is not only about consumption duration
When I was doing this research, I always heard: “There is no secret that people are watching TV in cold weather”. Well, yes but it was not my intention. My intention was also a bit more “healing effect” — If someone is in a very bad mood, let’s say suicidal, there is big harm and responsibility to recommend a movie/series which motivates suicide — That will be b***s**t recommendation.
I am a computer engineer, not a therapist or social study expert but I know from myself and people around me that we may be all in a bad mood, and media makes us worse or better. If you broke up with your girl/boyfriend and listens to painful songs, you will be more addicted to pain and this media consumption may drive you to depression. On the other hand, there are other types of media consumption that may make you more optimistic.
As a result, media consumption should not only be a time-consuming activity but may also heal some pains which are also very heavily related to weather. We all know when we are more melancholic or happy based on the weather.
Very Good Success
I am not a data scientist but a data engineer. My main responsibility is also guiding scientists with data. After a lot of discussions, we decided to do a test:
Recommendation Engine without weather parameters
Recommendation Engine with weather parameters
As we were a multinational and multiscreen IPTV/OTT product, we needed to take all the weather data where the user is watching from, not based on their nationality. There are error rates for specifying the geographic coordination from IP address based on device type (and unfortunately, based on country too) but it was worth trying.
This is famously called A/B testing and we started it for 30 days. We had several ratios to fill the recommendation bucket:
30% with weather parameters
60% with weather parameters
90% with weather parameters
We also followed the way of canary release that we had never served the new recommendation engine to 100% of the audience. We decided to serve it to 30% of the audience so the following data is based on 30% of our customers’ behaviors based on the ratios above:
Watching sessions are improved by 15%
Watching sessions are improved by 6%
Watching sessions are improved by -8%
(I did ceiling to the numbers) As we all can understand, when there are more recommendations based on weather data, watching sessions are getting lowered. It is understandable because people would like to see “similar” movies/series too.
But there was another catch that I also didn’t think of. Our comments in different app stores are also improved abnormally. Users were more likely to give us a positive feedback (based on sentiment analysis and stars).
Result
We released the new recommendation engine with 30% of weather data filled with strict relations on gender and age. It was a great success for all of us and of course, I also proved that the weirdest ideas may bring the most value sometimes.