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Profile-Based Suggestions

Personalized recommendations

The Spideo profiling system provides personalized recommendations across free and pay TV, based on the user’s activity:


- content watched

- ratings

- consulted pages

- favorites and playlists

- and many more configurable events


Generic profile-based suggestions

With collaborative filtering engines, unexplained personalized recommendations tend to look like a long and irrelevant list of the year’s blockbusters… With Spideo, the more interactions users have with content, the more we get an accurate knowledge of their tastes, interests and preferences. We can then translate this information into a personalized and dynamic list of relevant content according to the user’s history.


Battle

Users easily engage with content when we offer them a playful way to discover it. The “Movie Battle” feature asks very little from users but brings a major feedback. We ask users to choose among two movies; they can choose one, both or none. Based on their choices, Spideo recommends a personalized list of movies all sorted in thematic lists related to the user’s interests.


Hot or not?

Spideo’s Hot or Not feature combines personalization, time saving and swapping right weather users are in a hurry, lazy or just in the mood for singled-out relevant recommendations. The idea is to suggest a single content, show the connection with a previously watched content and display the main themes for more transparency. If users like the suggestions, they press play, if they don’t, they swipe right and go to the next recommendation.

Business

Spideo & Televisa

14 September 2017

TELEVISA selects Spideo’s recommendation and analytics platform for its SVOD service BLIMSeptember 12th, 2017, Paris / MexicoSpideo, leader in content...

Event

MEET SPIDEO at IBC 2017

14 September 2017

The Spideo Team will be in Amsterdam for the IBC.You will find us in Hall 14, in the beautiful booth H.03. 

Event

Visit Spideo at the 2017 NAB Show

11 April 2017

You will find us at Booth SU11807CM.We will be demonstrating our comprehensive recommendation solution: