Deep Neural Networks for YouTube Recommendations
Citations
376Â citations
Cites background from "Deep Neural Networks for YouTube Re..."
...from audio, video or textual item data [1, 5, 6, 10] and in particular for the problem of session-based recommendation [14, 15, 30, 38]....
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357Â citations
355Â citations
Cites methods from "Deep Neural Networks for YouTube Re..."
...mbines factorization machines for recommendation and deep learning for feature learning in a neural network architecture. (2) Using deep neural networks to model the interaction among users and items [3, 4, 8, 9]. For example, Neural Collaborative Filtering [8] replaces the inner product with a neural architecture to model the user-item interaction. The major difference between these methods and ours is that ...
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338Â citations
322Â citations
Cites background from "Deep Neural Networks for YouTube Re..."
...3098021 adopt machine learning as a tool to gain knowledge from data across a broad spectrum of use cases and products, ranging from recommender systems [6, 7], to clickthrough rate prediction for advertising [13, 15], and even the protection of endangered species [5]....
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References
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139,059Â citations
"Deep Neural Networks for YouTube Re..." refers background in this paper
...We observe that the most important signals are those that describe a user’s previous interaction with the item itself and other similar items, matching others’ experience in ranking ads [7]....
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30,843Â citations
24,012Â citations
"Deep Neural Networks for YouTube Re..." refers background in this paper
...A key advantage of using deep neural networks as a generalization of matrix factorization is that arbitrary continuous and categorical features can be easily added to the model....
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17,184Â citations
11,343Â citations