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A tensorflow implementation of DKN (Deep Knowledge-aware Network for News Recommendation)

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When I compile transE.cpp, I got a warning ![IMG_20200125_182418_485](https://user-images.githubusercontent.com/38836502/73124103-f15d6080-3f9f-11ea-9d0b-8e362901e3af.jpg). But I anyway run transE.exe, and see such a result ![IMG_20200125_182420_817](https://user-images.githubusercontent.com/38836502/73124156-b60f6180-3fa0-11ea-9bb9-c46a1996d7fd.jpg). Count of epoch is greater than 5000. It's normal? Do...

def get_local_word2entity(entities): # 格式:entity_id: entity """ Given the entities information in one line of the dataset, construct a map from word to entity index E.g., given entities = 'id_1:Harry Potter;id_2:England',...

问下raw_train.txt中的entity_id跟kg.txt中的entity_id和relation_id都是Microsoft Satori knowledge graph知识图谱中的吗?怎么下载这个知识图谱?

Thanks for your so great project. When I running TransE.cpp(others),there is a warning that "no return statement in function returning non-void". I read the code and found the function transetrainMode()...

王老师您好,我在评估模型时将通过sigmoid转换之后得到的预测概率值scores输出,发现他们全部大于0.5,请问模型划分正负例的阈值是多少呢?(应该不是0.5?) 例如某一个epoch得到的scores: [0.5778407 0.61155593 0.57902104 0.6150518 0.61513776 0.605235 0.5789735 0.57939625 0.58373684 0.5776857 0.5788436 0.5802991 0.58303684 0.57765114 0.58011097 0.5782152 0.58273846 0.59276307 0.5777418 0.5781118 0.58053976 0.5770587 0.5848505 0.6206129 0.5796546 0.5936298 0.5800667 0.5841336...

Hi , thanks for providing code. Could you please provide the complete dataset? any link or possible to email??

Hi, I tried to run the code you provided and compared the results mentioned on your paper. Results i got are not the same which are mentioned in the paper?...

I am new to Deep learning, I am getting error OSError: TransH_entity2vec_50.vec not found. prior to this step, everything run smoothly, any help appreciated

Dear authors, I found that the code implementation is slightly different from the structure presented in the paper (Figure 3). In the paper, both the output of the attention and...