Deep Neural Networks for YouTube Recommendations
Citations
545 citations
510 citations
Cites methods from "Deep Neural Networks for YouTube Re..."
...• The architecture of DeepWide and YouTubeNet is similar in the news recommendation scenario, thus we can observe comparable performance of the two methods....
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...For YouTubeNet, the dimension of final layer is set as 100....
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...DSSM outperforms DeepWide and YouTubeNet, the reason for which might be that DSSM models raw texts directly with word hashing....
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...For example, the AUC of KPCNN, DeepWide, and YouTubeNet increases by 1.1%, 1.8% and 1.1%, respectively....
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...• YouTubeNet [8] is proposed to recommend videos from a large-scale candidate set in YouTube using a deep candidate generation network and a deep ranking network....
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463 citations
Cites background from "Deep Neural Networks for YouTube Re..."
...Predicting click-through rates is important to many Internet companies, and various systems have been developed by different companies [8–10, 15, 21, 29, 43]....
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416 citations
Cites methods from "Deep Neural Networks for YouTube Re..."
...To keep our discussion uncluered, we have chosen a basic matrix factorisation model to implement, and it would be straightforward to replace it with more sophisticated models such as factorisation machines [33] or neural networks [8], whenever needed....
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413 citations
References
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139,059 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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30,843 citations
24,012 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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17,184 citations
11,343 citations