OTT推荐算法的艺术与科学
了解更多关于OTT推荐和发现 流媒体东部.
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Eric Schumacher-Rasmussen: 米歇尔, 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?
米歇尔·亚伯拉罕: 好吧, 肯定, using some sort of algorithms recommendation engine a part of most services and Jon and Peter are 正确的 in that. Oftentimes, there are reasons for what's shown at the top. 所有的分析都支持这一点, 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, 在某种程度上, an art as much as a science in making sure that you are pleasing the viewers as much as possible. 作为服务提供者, you have business reasons for recommending certain things--new titles that you want to promote, 你可能会得到报酬去推广这些东西, 或者原创内容.
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, 非常复杂, but the super-aggregators then need recommendation engines that are that much more sophisticated because they're dealing with that many more services, 更多的内容.
Eric Schumacher-Rasmussen: 当然, 在COVID大流行危机的早期, 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, 上午晚些时候, 下午早些时候, 等等, 我们被压扁了, 正确的? 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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