The Jakarta Post
In October 2016, Acast tested the new feature and discovered that 49 percent of its users engaged longer with a podcast that was selected through the Recommendations list. (Shutterstock/File)
Free podcasting application and web service Acast has upgraded the way users can access their preferred audio content.
With its new Recommendations feature, users can stop looking around categorized lists and directly check out podcasts that potentially interest them. It does this by using machine learning that realigns podcasts according to users’ tastes, by keeping track of their interests and listening habits. This algorithm can even offer shows from categories that are different to the one that users are currently tuning into.
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“For example, the Another Mother Runner podcast that talks about running, parenthood and work/life balance is categorized as ‘fitness and health’ based on the words that are visible and trackable. But Acast can generate recommendations based on Another Mother Runner for its listeners of shows that are actually about work/life balance, parenthood, running, without the show ever being tagged or categorized that way,” said the company's CTO and co-founder Johan Billgren as quoted by The Next Web.
In October 2016, the podcast hosting platform tested the new feature and discovered that 49 percent of its users engaged longer with a podcast that was selected through the Recommendations list. (nik/kes)