Comments (2)
Thanks for your reply, with attentive embedding propagation only used in recommendation training, in this way items with more user interactions will have more effective information propagation from other entities, it's not beneficial to cold start items who have very few user interactions, so with Attentive Embedding Propagation used in KGE, maybe we can get better performance especially for cold start items.
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Thanks for your interest. Blocking the attentive embedding propagation part from the KGE part is to reduce the memory-out error :( We are working on how to simplify KGAT. Thanks.
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Related Issues (20)
- I have a question about kg_final.txt HOT 1
- I have a question about memory growth HOT 1
- ERROR: loss@phase2 is nan HOT 1
- Yelp dataset is missing HOT 2
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