【Paper Note】Deep Session Interest Network for Click-Through Rate Prediction论文详解

比眉伴天荒 2021-11-29 14:56 248阅读 0赞

![在这里插入图片描述][20190722113354909.png]

### 介绍 ###

通常来讲，在用户的行为序列中，每个会话（Session）的行为是相似的，在不同的会话之间差异较大，阿里的这篇论文中，根据时间间隔来定义会话，将用户点击行为按照时间排序，如果两个行为的时间间隔大于30min则进行切分。如下图所示：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9yb2d1ZXNpci5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70]  
从图中可以观察，第一个会话中，用户点击的feed为裤子，第二个则是首饰，第三个是衣服，符合上面说的会话的特征。DSIN就是基于此进行用户行为建模。  
DSIN模型的几点贡献：

*  会话内同质，会话间异质
 *  在DSIN中使用了self-attention、bias embedding、Bi-LSTM，模型更能捕捉不同会话间的用户兴趣偏好

### 模型介绍 ###

#### Base Model ####

Base Model是一个全连接网络。  
特征主要由三部分：user特征、item特征，用户行为特征。user特征主要是性别、城市等，item特征主要是id，品牌等，用户行为特征主要是用户点击行为。此外，在代码中，稠密特征是加在模型的最后的，后面代码中会有说明。  
embedding主要将稀疏特征编码成稠密向量。  
Loss Function为negative log-likelihood function：  
 (1) L = − 1 N ∑ ( x , y ) ∈ D ( y   l o g   p ( x ) + ( 1 − y )   l o g ( 1 − p ( x ) ) ) L=-\\frac\{1\}\{N\}\\sum\_\{(x,y)\\in D\}\{(y\\ log\\ p(x)+(1-y)\\ log(1-p(x)))\} \\tag\{1\} L=−N1(x,y)∈D∑(y log p(x)\+(1−y) log(1−p(x)))(1)

#### 模型 ####

模型总体架构如下：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9yb2d1ZXNpci5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 1]  
全连接之前，主要由以下两部分：左边user特征和item特征的向量表示，生成embedding向量；右边是对用户行为处理部分，该部分主要分为四层：

##### （1）Session Division Layer #####

对用户的历史行为序列进行切分，按照时间排序，时间间隔大于30min则进行切分。论文中将用户行为序列S切分成K个会话Q，第k个会话为： Q k = \[ b 1 ; b 2 ; ⋯ &ThinSpace; ; b i ; ⋯ &ThinSpace; ; b T \] Q\_k=\[b\_1;b\_2;\\cdots;b\_i;\\cdots;b\_T\] Qk=\[b1;b2;⋯;bi;⋯;bT\]，其中， T T T为会话长度， b i b\_i bi是会话中第i个行为，并且， b i ∈ R d b\_i\\in R^d bi∈Rd， Q k ∈ R T ∗ d Q\_k\\in R^\{T\*d\} Qk∈RT∗d，所以 Q ∈ R K ∗ T ∗ d Q\\in R^\{K\*T\*d\} Q∈RK∗T∗d  
![在这里插入图片描述][20190731205702315.png]

##### （2）Session Interest Extractor Layer #####

论文中对每个会话进行Transformer处理，可通过代码得知：

# Session Interest Extractor Layer
        Self_Attention = Transformer(att_embedding_size,
                                     att_head_num,
                                     dropout_rate=0,
                                     use_layer_norm=False,
                                     use_positional_encoding=(not bias_encoding),  # bias_encoding=False
                                     seed=seed,
                                     supports_masking=True,
                                     blinding=True)

在Transformer过程中，进行了positional\_encoding处理，也就是论文中说的Bias Embedding，定义如下：  
 (2) B E ( k , t , c ) = w k K + w t T + w c C BE\_\{(k,t,c)\}=w^K\_k+w^T\_t+w^C\_c \\tag\{2\} BE(k,t,c)=wkK\+wtT\+wcC(2)  
 B E ∈ R K ∗ T ∗ d BE\\in R^\{K \* T \* d\} BE∈RK∗T∗d，和Q的维度相同。 B E ( k , t , c ) BE\_\{(k,t,c)\} BE(k,t,c)表示第k个session中，第t个物品的embedding向量的第c个位置的偏置项，也就是说，每个会话、会话中的每个物品有偏置项外，每个物品对应的embedding的每个位置，都加入了偏置项。所以加入偏置项后，Q变为：  
 (3) Q = Q + B E Q=Q+BE \\tag\{3\} Q=Q\+BE(3)  
随后进行Transformer处理，输出为用户的兴趣向量：  
 (4) I k = A v g ( I k Q ) I\_k=Avg(I^Q\_k) \\tag\{4\} Ik=Avg(IkQ)(4)  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9yb2d1ZXNpci5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 2]

##### （3）Session Interest Interacting Layer #####

兴趣交互层使用Bi-LSTM，具体的计算流程如下：  
 (5) i t = σ ( W x i I t + W h i h t − 1 + W c i c t − 1 + b i ) i\_t=\\sigma(W\_\{xi\}I\_t+W\_\{hi\}h\_\{t-1\}+W\_\{ci\}c\_\{t-1\}+b\_i) \\tag\{5\} it=σ(WxiIt\+Whiht−1\+Wcict−1\+bi)(5)  (6) f t = σ ( W x f I t + W h f h t − 1 + W c f c t − 1 + b f ) f\_t=\\sigma(W\_\{xf\}I\_t+W\_\{hf\}h\_\{t-1\}+W\_\{cf\}c\_\{t-1\}+b\_f) \\tag\{6\} ft=σ(WxfIt\+Whfht−1\+Wcfct−1\+bf)(6)  (7) c t = f t c t − 1 + i t   t a n h ( W x c I t + W h c h t − 1 + b c ) c\_t=f\_tc\_\{t-1\}+i\_t\\ tanh(W\_\{xc\}I\_t+W\_\{hc\}h\_\{t-1\}+b\_c) \\tag\{7\} ct=ftct−1\+it tanh(WxcIt\+Whcht−1\+bc)(7)  (8) o t = σ ( W x o I t + W h o h t − 1 + W c o c t − 1 + b c ) o\_t=\\sigma(W\_\{xo\}I\_t+W\_\{ho\}h\_\{t-1\}+W\_\{co\}c\_\{t-1\}+b\_c) \\tag\{8\} ot=σ(WxoIt\+Whoht−1\+Wcoct−1\+bc)(8)  (9) h t = o t   t a n h ( c t ) h\_t=o\_t\\ tanh(c\_t) \\tag\{9\} ht=ot tanh(ct)(9)  
每个时刻的hidden state为前向传播和反向传播的hidden state的和。  
![在这里插入图片描述][20190731205731777.png]

##### （4）Session Interest Activating Layer #####

激活单元使用注意力机制：  
 (10) a k I = e x p ( I k W I X I ) ∑ k K e x p ( I k W I X I ) a^I\_k=\\frac\{exp(I\_kW^IX^I)\}\{\\sum^K\_k exp(I\_kW^IX^I)\} \\tag\{10\} akI=∑kKexp(IkWIXI)exp(IkWIXI)(10)  (11) U I = ∑ k K a k I I k U^I=\\sum^K\_k\{a^I\_kI\_k\} \\tag\{11\} UI=k∑KakIIk(11)  (12) a k H = e x p ( H k W H X I ) ∑ k K e x p ( H k W H X I ) a^H\_k=\\frac\{exp(H\_kW^HX^I)\}\{\\sum^K\_k exp(H\_kW^HX^I)\} \\tag\{12\} akH=∑kKexp(HkWHXI)exp(HkWHXI)(12)  (13) U H = ∑ k K a k H H k U^H=\\sum^K\_k\{a^H\_kH\_k\} \\tag\{13\} UH=k∑KakHHk(13)  
![在这里插入图片描述][20190731205902273.png]

### 代码 ###

代码部分主要介绍模型主体结构，训练脚本不做说明。GitHub地址：[https://github.com/shenweichen/DSIN][https_github.com_shenweichen_DSIN]

# coding: utf-8
    """ Author: Weichen Shen,wcshen1994@163.com Reference: [1] Feng Y, Lv F, Shen W, et al. Deep Session Interest Network for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1905.06482, 2019.(https://arxiv.org/abs/1905.06482) """
    
    from collections import OrderedDict
    
    from tensorflow.python.keras.initializers import RandomNormal
    from tensorflow.python.keras.layers import (Concatenate, Dense, Embedding,
                                                Flatten, Input)
    from tensorflow.python.keras.models import Model
    from tensorflow.python.keras.regularizers import l2
    
    from deepctr.input_embedding import (create_singlefeat_inputdict,
                                   get_embedding_vec_list, get_inputs_list)
    from deepctr.layers.core import DNN, PredictionLayer
    from deepctr.layers.sequence import (AttentionSequencePoolingLayer, BiasEncoding,
                                   BiLSTM, Transformer)
    from deepctr.layers.utils import NoMask, concat_fun
    from deepctr.utils import check_feature_config_dict
    
    
    def DSIN(feature_dim_dict, sess_feature_list, embedding_size=8, sess_max_count=5, sess_len_max=10, bias_encoding=False,
             att_embedding_size=1, att_head_num=8, dnn_hidden_units=(200, 80), dnn_activation='sigmoid', dnn_dropout=0,
             dnn_use_bn=False, l2_reg_dnn=0, l2_reg_embedding=1e-6, init_std=0.0001, seed=1024, task='binary',
             ):
        """Instantiates the Deep Session Interest Network architecture. :param feature_dim_dict: dict,to indicate sparse field (**now only support sparse feature**)like {'sparse':{'field_1':4,'field_2':3,'field_3':2},'dense':[]} :param sess_feature_list: list,to indicate session feature sparse field (**now only support sparse feature**),must be a subset of ``feature_dim_dict["sparse"]`` :param embedding_size: positive integer,sparse feature embedding_size. :param sess_max_count: positive int, to indicate the max number of sessions :param sess_len_max: positive int, to indicate the max length of each session :param bias_encoding: bool. Whether use bias encoding or postional encoding :param att_embedding_size: positive int, the embedding size of each attention head :param att_head_num: positive int, the number of attention head :param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of deep net :param dnn_activation: Activation function to use in deep net :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate. :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in deep net :param l2_reg_dnn: float. L2 regularizer strength applied to DNN :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector :param init_std: float,to use as the initialize std of embedding vector :param seed: integer ,to use as random seed. :param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss :return: A Keras model instance. """
        check_feature_config_dict(feature_dim_dict)
    
        if (att_embedding_size * att_head_num != len(sess_feature_list) * embedding_size):
            raise ValueError(
                "len(session_feature_lsit) * embedding_size must equal to att_embedding_size * att_head_num ,got %d * %d != %d *%d" % (
                len(sess_feature_list), embedding_size, att_embedding_size, att_head_num))
    
        sparse_input, dense_input, user_behavior_input_dict, _, user_sess_length = get_input(feature_dim_dict, sess_feature_list, sess_max_count, sess_len_max)
    
        # 生成User Field和Item Field
        sparse_embedding_dict = { feat.name: Embedding(feat.dimension, 
                                                      embedding_size,
                                                      embeddings_initializer=RandomNormal(mean=0.0,
                                                                                          stddev=init_std, 
                                                                                          seed=seed),
                                                      embeddings_regularizer=l2(l2_reg_embedding),
                                                      name='sparse_emb_' + str(i) + '-' + feat.name,
                                                      mask_zero=(feat.name in sess_feature_list)) for i, feat in enumerate(feature_dim_dict["sparse"])}
    
        query_emb_list = get_embedding_vec_list(sparse_embedding_dict, 
                                                sparse_input, 
                                                feature_dim_dict["sparse"],
                                                sess_feature_list, 
                                                sess_feature_list)
        # concat User Field和Item Field
        query_emb = concat_fun(query_emb_list)
    
        deep_input_emb_list = get_embedding_vec_list(sparse_embedding_dict, 
                                                     sparse_input, 
                                                     feature_dim_dict["sparse"],
                                                     mask_feat_list=sess_feature_list)
        deep_input_emb = concat_fun(deep_input_emb_list)
        deep_input_emb = Flatten()(NoMask()(deep_input_emb))
    
        # Session Divsion Layer
        tr_input = sess_interest_division(sparse_embedding_dict,
                                          user_behavior_input_dict,
                                          feature_dim_dict['sparse'],
                                          sess_feature_list,
                                          sess_max_count,
                                          bias_encoding=bias_encoding)
    
        # Session Interest Extractor Layer
        Self_Attention = Transformer(att_embedding_size,
                                     att_head_num,
                                     dropout_rate=0,
                                     use_layer_norm=False,
                                     use_positional_encoding=(not bias_encoding),  # bias_encoding=False
                                     seed=seed,
                                     supports_masking=True,
                                     blinding=True)
    
        sess_fea = sess_interest_extractor(tr_input,
                                           sess_max_count,
                                           Self_Attention)
    
        # Session Interest Interacting Layer
        lstm_outputs = BiLSTM(len(sess_feature_list) * embedding_size,
                              layers=2,
                              res_layers=0,
                              dropout_rate=0.2, )(sess_fea)
    
        # Session Interest Activating Layer
        # 黄色Activation Unit
        interest_attention_layer = AttentionSequencePoolingLayer(att_hidden_units=(64, 16),
                                                                 weight_normalization=True,
                                                                 supports_masking=False)([query_emb, sess_fea, user_sess_length])
        # 蓝色Activation Unit
        lstm_attention_layer = AttentionSequencePoolingLayer(att_hidden_units=(64, 16),
                                                             weight_normalization=True)([query_emb, lstm_outputs, user_sess_length])
    
        deep_input_emb = Concatenate()([deep_input_emb, Flatten()(interest_attention_layer), Flatten()(lstm_attention_layer)])
    
        # 如果有dense输入，和上面两部分一起concat，输入到DNN中
        if len(dense_input) > 0:
            deep_input_emb = Concatenate()(
                [deep_input_emb] + list(dense_input.values()))
    
        output = DNN(dnn_hidden_units,
                     dnn_activation,
                     l2_reg_dnn,
                     dnn_dropout,
                     dnn_use_bn,
                     seed)(deep_input_emb)
    
        output = Dense(1, use_bias=False, activation=None)(output)
        output = PredictionLayer(task)(output)
    
        sess_input_list = []
        # sess_input_length_list = []
        for i in range(sess_max_count):
            sess_name = "sess_" + str(i)
            sess_input_list.extend(get_inputs_list([user_behavior_input_dict[sess_name]]))
            # sess_input_length_list.append(user_behavior_length_dict[sess_name])
    
        model_input_list = get_inputs_list([sparse_input, dense_input]) + sess_input_list + [user_sess_length]
    
        model = Model(inputs=model_input_list, outputs=output)
    
        return model
    
    
    def get_input(feature_dim_dict, seq_feature_list, sess_max_count, seq_max_len):
        sparse_input, dense_input = create_singlefeat_inputdict(feature_dim_dict)
        user_behavior_input = { }
        for idx in range(sess_max_count):
            sess_input = OrderedDict()
            for i, feat in enumerate(seq_feature_list):
                sess_input[feat] = Input(
                    shape=(seq_max_len,), name='seq_' + str(idx) + str(i) + '-' + feat)
    
            user_behavior_input["sess_" + str(idx)] = sess_input
    
        user_behavior_length = { "sess_" + str(idx): Input(shape=(1,), name='seq_length' + str(idx)) for idx in
                                range(sess_max_count)}
        user_sess_length = Input(shape=(1,), name='sess_length')
    
        return sparse_input, dense_input, user_behavior_input, user_behavior_length, user_sess_length
    
    
    def sess_interest_division(sparse_embedding_dict, user_behavior_input_dict, sparse_fg_list, sess_feture_list,
                               sess_max_count,
                               bias_encoding=True):
        tr_input = []
        for i in range(sess_max_count):
            sess_name = "sess_" + str(i)
            keys_emb_list = get_embedding_vec_list(sparse_embedding_dict, 
                                                   user_behavior_input_dict[sess_name],
                                                   sparse_fg_list, 
                                                   sess_feture_list, 
                                                   sess_feture_list)
            # [sparse_embedding_dict[feat](user_behavior_input_dict[sess_name][feat]) for feat in
            # sess_feture_list]
            keys_emb = concat_fun(keys_emb_list)
            tr_input.append(keys_emb)
        if bias_encoding:
            tr_input = BiasEncoding(sess_max_count)(tr_input)
        return tr_input
    
    
    def sess_interest_extractor(tr_input, sess_max_count, TR):
        tr_out = []
        for i in range(sess_max_count):
            tr_out.append(TR(
                [tr_input[i], tr_input[i]]))
        sess_fea = concat_fun(tr_out, axis=1)
        return sess_fea

*  更新时间：2019-07-31

[20190722113354909.png]: /images/20211129/e3ab25fb5cf847259bf364959b756094.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9yb2d1ZXNpci5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70]: /images/20211129/4d20f7627601473fae2cffd7add784c6.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9yb2d1ZXNpci5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 1]: /images/20211129/52435e19432948d6b312990a88cd8e11.png
[20190731205702315.png]: /images/20211129/d3e32470eeaa47098d3d01da41a36a03.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9yb2d1ZXNpci5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 2]: /images/20211129/89c2d02ca0c04e1cadf9a8c05ed2fcc8.png
[20190731205731777.png]: /images/20211129/d885fcee8bee422090ce3620e5890a12.png
[20190731205902273.png]: /images/20211129/e34de4abbdf845dc983fdc1cb638d9b6.png
[https_github.com_shenweichen_DSIN]: https://github.com/shenweichen/DSIN