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The study noted that YouTube’s recommendation algorithm “drives 70% of all video views.” ...
Algorithmic radicalization is the concept that recommender algorithms on popular social media sites such as YouTube and Facebook drive users toward progressively more extreme content over time, leading to them developing radicalized extremist political views. Algorithms record user interactions, from likes/dislikes to amount of time spent on ...
The user based top-N recommendation algorithm uses a similarity-based vector model to identify the k most similar users to an active user. After the k most similar users are found, their corresponding user-item matrices are aggregated to identify the set of items to be recommended.
The researchers then took note of the top 10 recommendations in the “Up Next” panel to the right of the video. They carried out the simulation 100 times, analyzing 1,000 videos in total.
A recommender system (RecSys), or a recommendation system (sometimes replacing system with terms such as platform, engine, or algorithm), is a subclass of information filtering system that provides suggestions for items that are most pertinent to a particular user.
A convolutional neural network (CNN) is a regularized type of feedforward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [1]
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For years YouTube's video-recommending algorithm has stood accused of fuelling a grab bag of societal ills by feeding users an AI-amplified diet of hate speech, political extremism and/or ...