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A deep matching model library for recommendations & advertising. It's easy to train models and to export representation vectors which can be used for ANN search.

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Please refer to the [FAQ](https://deepmatch.readthedocs.io/en/latest/FAQ.html) in doc and search for the [related issues](https://github.com/shenweichen/DeepMatch/issues) before you ask the question. **Describe the question(问题描述)** 在 create_embedding_dict 函数中,使用特征的embedding_name 作为key来进行存储,那么如果特征的embedding_name 重复,事实上在例如在run_sdm里面用户序列中变长特征和item特征就是重复的。这样的话会导致得到的emd被覆盖,如果只需要一份embedding的话,那这样写还有什么意义? ``` def create_embedding_dict(sparse_feature_columns, varlen_sparse_feature_columns,...

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output: Input In [12], in 77 if tf.__version__ >= '2.0.0': 78 tf.compat.v1.disable_eager_execution() ---> 82 model = YoutubeDNN(user_feature_columns, item_feature_columns, num_sampled=5, user_dnn_hidden_units=(64, embedding_dim)) 83 #model = MIND(user_feature_columns,item_feature_columns,dynamic_k=False,p=1,k_max=2,num_sampled=5,user_dnn_hidden_units=(64, embedding_dim)) 85 model.compile(optimizer="adam", loss=sampledsoftmaxloss) #...

help wanted

hi,dear In the [code](https://github.com/shenweichen/DeepMatch/blob/master/examples/run_youtubednn_sampledsoftmax.py#L13), could you tell me the data from where? want more data to have a try, thx

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Implement GRU4REC model following by 'Session-based Recommendations with Recurrent Neural Networks'. Test the model performance against MovieLen 1M, achieve recall@50 = 0.19 (the test script is in ./examples/run_gru4rec.py).

Please refer to the [FAQ](https://deepmatch.readthedocs.io/en/latest/FAQ.html) in doc and search for the [related issues](https://github.com/shenweichen/DeepMatch/issues) before you ask the question. **Describe the question(问题描述)** 对deepmatch中的sdm模型进行模型存储和加载的尝试,出现 AttributeError: 'Functional' object has no attribute 'user_input'的错误,求助各路大神.谢谢~ **Additional...

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**Describe the question(问题描述)** After I saved SDM weights and loaded it in another process. It produced different user embeddings. How to save SDM model properly and then load it properly...

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I wonder how ncf is being used in retrieving and which layer is its item_embedding/user_embedding? I didn't find it in ncf code.

Please refer to the [FAQ](https://deepmatch.readthedocs.io/en/latest/FAQ.html) in doc and search for the [related issues](https://github.com/shenweichen/DeepMatch/issues) before you ask the question. **Describe the question(问题描述)** VaLueError: '’. seq. emb. hist movie. _id' is not...

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大数据场景下全量加载到显卡训练显然不现实,鄙人在TF2.4.0中试了下将数据处理好后分批给到DeepMatch模型,发现有以下问题: **1. 通过tf.data.Dataset.from_tensor_slices将数据分批后训练,却发现在tf.compat.v1.disable_eager_execution()后无法使用,报错如下:** D:\Anaconda3\envs\TF2GPU\lib\site-packages\tensorflow\python\keras\backend.py:434: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method...

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