Add SDM model

shenweichen · May 17, 2020 · ea69d9c · ea69d9c
2 parents cc8c1a9 + fe1e7b3
commit ea69d9c
Show file tree

Hide file tree

Showing 27 changed files with 1,083 additions and 32 deletions.
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
@@ -18,7 +18,7 @@ jobs:
     strategy:
       matrix:
         python-version: [3.5,3.6,3.7]
-        tf-version: [1.4.0,1.14.0,2.1.0]
+        tf-version: [1.4.0,1.14.0,2.1.0,2.2.0]
 
         exclude:
           - python-version: 3.7

diff --git a/README.md b/README.md
@@ -28,7 +28,8 @@ Let's [**Get Started!**](https://deepmatch.readthedocs.io/en/latest/Quick-Start.
 | DSSM | [CIKM 2013][Deep Structured Semantic Models for Web Search using Clickthrough Data](https://www.microsoft.com/en-us/research/publication/learning-deep-structured-semantic-models-for-web-search-using-clickthrough-data/)    |
 | YoutubeDNN     | [RecSys 2016][Deep Neural Networks for YouTube Recommendations](https://www.researchgate.net/publication/307573656_Deep_Neural_Networks_for_YouTube_Recommendations)            |
 | NCF  | [WWW 2017][Neural Collaborative Filtering](https://arxiv.org/abs/1708.05031)       |
-| MIND  | [CIKM 2019][Multi-interest network with dynamic routing for recommendation at Tmall](https://arxiv.org/pdf/1904.08030)  |
+| SDM  | [CIKM 2019][SDM: Sequential Deep Matching Model for Online Large-scale Recommender System](https://arxiv.org/abs/1909.00385)  |
+| MIND | [CIKM 2019][Multi-interest network with dynamic routing for recommendation at Tmall](https://arxiv.org/pdf/1904.08030)  |
 
 ## Contributors([welcome to join us!](./CONTRIBUTING.md))
 

diff --git a/deepmatch/__init__.py b/deepmatch/__init__.py
@@ -1,4 +1,4 @@
 from .utils import check_version
 
-__version__ = '0.1.2'
+__version__ = '0.1.3'
 check_version(__version__)
diff --git a/deepmatch/layers/__init__.py b/deepmatch/layers/__init__.py
@@ -3,6 +3,9 @@
 
 from .core import PoolingLayer, Similarity, LabelAwareAttention, CapsuleLayer,SampledSoftmaxLayer,EmbeddingIndex
 from ..utils import sampledsoftmaxloss
+from .interaction import DotAttention, ConcatAttention, SoftmaxWeightedSum, AttentionSequencePoolingLayer, SelfAttention,\
+    SelfMultiHeadAttention, UserAttention
+from .sequence import DynamicMultiRNN
 
 _custom_objects = {'PoolingLayer': PoolingLayer,
                    'Similarity': Similarity,
@@ -11,7 +14,15 @@
                    'reduce_sum':reduce_sum,
                    'SampledSoftmaxLayer':SampledSoftmaxLayer,
                    'sampledsoftmaxloss':sampledsoftmaxloss,
-                   'EmbeddingIndex':EmbeddingIndex
+                   'EmbeddingIndex':EmbeddingIndex,
+                   'DotAttention':DotAttention,
+                   'ConcatAttention':ConcatAttention,
+                   'SoftmaxWeightedSum':SoftmaxWeightedSum,
+                   'AttentionSequencePoolingLayer':AttentionSequencePoolingLayer,
+                   'SelfAttention':SelfAttention,
+                   'SelfMultiHeadAttention':SelfMultiHeadAttention,
+                   'UserAttention':UserAttention,
+                    'DynamicMultiRNN':DynamicMultiRNN
                    }
 
 custom_objects = dict(custom_objects, **_custom_objects)