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DKN improve #1722

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merged 4 commits into from
Jun 2, 2022
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DKN improve #1722

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DKN improve
miguelgfierro May 24, 2022
729b0d6
Update dkn_deep_dive.ipynb
miguelgfierro May 24, 2022
efc92da
Merge branch 'staging' into miguelgfierro-patch-1
miguelgfierro May 30, 2022
5cbeeff
Merge branch 'staging' into miguelgfierro-patch-1
miguelgfierro Jun 1, 2022
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6 changes: 3 additions & 3 deletions examples/02_model_content_based_filtering/dkn_deep_dive.ipynb
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"\n",
"- DKN is a content-based deep model for CTR prediction rather than traditional ID-based collaborative filtering. \n",
"- It makes use of knowledge entities and common sense in news content via joint learning from semantic-level and knowledge-level representations of news articles.\n",
"- DKN uses an attention module to dynamically calculate a user's aggregated historical representaition.\n",
"- DKN uses an attention module to dynamically calculate a user's aggregated historical representation.\n",
"\n",
"\n",
"## Data format\n",
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"source": [
"## References\n",
"\n",
"\\[1\\] Wang, Hongwei, et al. \"DKN: Deep Knowledge-Aware Network for News Recommendation.\" Proceedings of the 2018 World Wide Web Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2018.<br>\n",
"\\[1\\] Hongwei Wang, Fuzheng Zhang, Xing Xie and Minyi Guo, \"DKN: Deep Knowledge-Aware Network for News Recommendation\", in Proceedings of the 2018 World Wide Web Conference (WWW), 2018, https://arxiv.org/abs/1801.08284. <br>\n",
"\\[2\\] Knowledge Graph Embeddings including TransE, TransH, TransR and PTransE. https://github.com/thunlp/KB2E <br>\n",
"\\[3\\] Wu, Fangzhao, et al. \"MIND: A Large-scale Dataset for News Recommendation\" Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. https://msnews.github.io/competition.html <br>\n",
"\\[3\\] Fangzhao Wu et al., \"MIND: A Large-scale Dataset for News Recommendation\", Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020, https://msnews.github.io/competition.html. <br>\n",
"\\[4\\] GloVe: Global Vectors for Word Representation. https://nlp.stanford.edu/projects/glove/"
]
}
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