The Art and Science of OTT Recommendation Algorithms
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Read the complete transcript of this video:
Eric Schumacher-Rasmussen: Michelle, what are you seeing as you talk to services in the industry and technology providers, in terms of improving the types of recommendations that are delivered to viewers and helping them find the content they want to watch?
Michelle Abraham: Well, definitely, using some sort of algorithms recommendation engine a part of most services and Jon and Peter are right in that. Oftentimes, there are reasons for what's shown at the top. There's all of the analytics that support that, there are business reasons for recommending certain shows over others that don't necessarily factor into what the consumer has actually watched in the past. So, you do find that there's a desire to promote new content, whether it necessarily fits with what the consumers watched or not. So using a recommendation engine and it's algorithms has really become, to a certain extent, an art as much as a science in making sure that you are pleasing the viewers as much as possible. As a service provider, you have business reasons for recommending certain things--new titles that you want to promote, things that maybe you're getting paid to promote, or original content.
But also, it needs to be mixed in with what fits in with what the consumers want to watch. Recommendations are getting very sophisticated based on time of day so that they know that the viewer, in the middle of the morning might tune in to a certain type of content. Whereas in the evening they would be tuning in to something different. So, a lot of sophistication, but the super-aggregators then need recommendation engines that are that much more sophisticated because they're dealing with that many more services, that much more content.
Eric Schumacher-Rasmussen: Of course, early on in the COVID pandemic crisis, when more stay-at-home orders were in effect in more states, we saw that the old reliable day parts in terms of viewing in the morning, late morning, early afternoon, et cetera, were getting flattened, right? That people were viewing content all throughout the day, and the types of content they were viewing were not as predictable as they had been for a while. And so algorithms and recommendation engines have to work to keep up with that.
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