Netflix Recommendations: Controlled or Imposed

Netflix Recommendations: Controlled or Imposed

Netflix Inc. is American premier shows and movie streaming company. Unequivocally, “first mover advantage” is the major rationale behind this company subjugating the market. Netflix has a myriad of movies and shows to watch from. Not to overlook, the content here updates from time to time. The oodles of genres being its best quirk, the application is, however, worth a king’s ransom. Nearly more than 80% of its users view the shows recommended to them by the site itself. The influence of such recommendations is the actual reason behind this extreme turnaround.

 

Those suggestions play an integral role in retaining customers and improving the user experience, according to Carlos Gomez Uribe, the man whose factions create the entangled algorithms that help members find content that gratifies their diacritic tastes. These complex algorithms yield different results for distinct users if each of them watched the same ‘Arrested Development’, for example. These algorithms may be the result of their previous searches, their scroll timings and many others.

 

“Our algorithms have to be good enough to be able to compute all the recommendations and all the previous viewing experiences for these profiles, many of which use different devices,” exclaimed Carlos Gomez, vice president of product innovation at Netflix, who was the featured elocutionist at Northeastern University’s Profiles in innovation presidential speaker series. He said “So the scale of this computation is pretty large.” Gomez Uribe acclaimed that the process to flourish recommendation algorithms boils down to scientific method, with a particular credence on experimentation and state-of-the-art statistical and mathematical models.

 

Harminder Singh

 

 

Harminder Singh
Harminder Singh

harminder.happy01@gmail.com

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