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add news rec methods NAML NRMS NPA LSTUR #1080
add news rec methods NAML NRMS NPA LSTUR #1080
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Check out this pull request on You'll be able to see Jupyter notebook diff and discuss changes. Powered by ReviewNB. |
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Thanks for the codes! In general it looks good to me. Just provided some minor comments.
Btw, the tests on Spark environment failed and I believe there is no Spark related code submitted in the PR, is that correct?
Right now the test machines are down because of the measurements with covid in all MS. We will need to run the tests ourselves. I will add more folks to comment in the review |
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I took a look, this is really impressive guys. My honest congratulations. It is absolutely awesome to be collaborating with you. Please send my regards to Xing.
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Nice additions! I added some small suggestions / questions.
README.md
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| Neural News Recommendation with Long- and Short-term User Representations (LSTUR) | [Python CPU / Python GPU](notebooks/00_quick_start/lstur_synthetic.ipynb) | Content-Based Filtering | Deep learning news recommendation algorithm considering users' long- and short-term preference | | ||
| Neural News Recommendation with Attentive Multi-View Learning (NAML) | [Python CPU / Python GPU](notebooks/00_quick_start/naml_synthetic.ipynb) | Content-Based Filtering | Deep learning news recommendation algorithm with multi-view learning | | ||
| Neural Collaborative Filtering (NCF) | [Python CPU / Python GPU](notebooks/00_quick_start/ncf_movielens.ipynb) | Collaborative Filtering | Deep learning algorithm with enhanced performance for implicit feedback | | ||
| Neural News Recommendation with Personalized Attention (NPA) | [Python CPU / Python GPU](notebooks/00_quick_start/npa_synthetic.ipynb) | Content-Based Filtering | Deep learning news recommendation algorithm ultilizing personalized attention | | ||
| Neural News Recommendation with Multi-Head Self-Attention (NRMS) | [Python CPU / Python GPU](notebooks/00_quick_start/nrms_synthetic.ipynbb) | Content-Based Filtering | Deep learning news recommendation algorithm ultilizing multi-head self-attention | |
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The rest of the algorithms in this table are not domain specific. Is it important to have the news focus here? It may lead to people overlooking it as an option for other domains.
return [imp_indexes, user_indexes, click_news_index_batch, candidate_news_index_batch], labels | ||
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class NewsTestIterator(BaseIterator): |
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is this the same as NewsTrainIterator with npratio=0? Do we need separate classes? Or can we extend that class to reduce redundant code?
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Thanks for the suggestions. I will try to change the code and combine these two iterators.
Co-Authored-By: Scott Graham <[email protected]>
Co-Authored-By: Scott Graham <[email protected]>
Co-Authored-By: Scott Graham <[email protected]>
hey @yjw1029 @yueguoguo @gramhagen @anargyri, just FYI, I run all the tests in Prometheus, new and old ones and everything passes |
…w1029/recommenders into v-jinyi/add-news-reco-methods
hi @miguelgfierro. Thanks for your comment. I change the problems you mentioned above😁. Are there any issues in the code? |
@yjw1029 from my part this is ready to go. @gramhagen is there anything else that you would like to review? |
after speaking with @gramhagen, I'll merge this. @yjw1029 amazing work |
Description
Add news rec methods (NAML NRMS NPA LSTUR)
Related Issues
#1079
Checklist:
staging
and notmaster
.