Examples

DSSM with negative sampling

The MovieLens data has been used for personalized tag recommendation,which contains 668, 953 tag applications of users on movies. Here is a small fraction of data include only sparse field.

image

This example shows how to use DSSM to solve a matching task. You can get the demo data movielens_sample.txt and run the following codes.

import pandas as pd
from deepctr.inputs import SparseFeat, VarLenSparseFeat
from preprocess import gen_data_set, gen_model_input
from sklearn.preprocessing import LabelEncoder
from tensorflow.python.keras.models import Model

from deepmatch.models import *

if __name__ == "__main__":

    data = pd.read_csvdata = pd.read_csv("./movielens_sample.txt")
    sparse_features = ["movie_id", "user_id",
                       "gender", "age", "occupation", "zip", ]
    SEQ_LEN = 50
    negsample = 3

    # 1.Label Encoding for sparse features,and process sequence features with `gen_date_set` and `gen_model_input`

    features = ['user_id', 'movie_id', 'gender', 'age', 'occupation', 'zip']
    feature_max_idx = {}
    for feature in features:
        lbe = LabelEncoder()
        data[feature] = lbe.fit_transform(data[feature]) + 1
        feature_max_idx[feature] = data[feature].max() + 1

    user_profile = data[["user_id", "gender", "age", "occupation", "zip"]].drop_duplicates('user_id')

    item_profile = data[["movie_id"]].drop_duplicates('movie_id')

    user_profile.set_index("user_id", inplace=True)

    user_item_list = data.groupby("user_id")['movie_id'].apply(list)

    train_set, test_set = gen_data_set(data, negsample)

    train_model_input, train_label = gen_model_input(train_set, user_profile, SEQ_LEN)
    test_model_input, test_label = gen_model_input(test_set, user_profile, SEQ_LEN)

    # 2.count #unique features for each sparse field and generate feature config for sequence feature

    embedding_dim = 16

    user_feature_columns = [SparseFeat('user_id', feature_max_idx['user_id'], embedding_dim),
                            SparseFeat("gender", feature_max_idx['gender'], embedding_dim),
                            SparseFeat("age", feature_max_idx['age'], embedding_dim),
                            SparseFeat("occupation", feature_max_idx['occupation'], embedding_dim),
                            SparseFeat("zip", feature_max_idx['zip'], embedding_dim),
                            VarLenSparseFeat(SparseFeat('hist_movie_id', feature_max_idx['movie_id'], embedding_dim,
                                                        embedding_name="movie_id"), SEQ_LEN, 'mean', 'hist_len'),
                            ]

    item_feature_columns = [SparseFeat('movie_id', feature_max_idx['movie_id'], embedding_dim)]

    # 3.Define Model and train

    model = DSSM(user_feature_columns, item_feature_columns)  # FM(user_feature_columns,item_feature_columns)

    model.compile(optimizer='adagrad', loss="binary_crossentropy")

    history = model.fit(train_model_input, train_label,  # train_label,
                        batch_size=256, epochs=1, verbose=1, validation_split=0.0, )

    # 4. Generate user features for testing and full item features for retrieval
    test_user_model_input = test_model_input
    all_item_model_input = {"movie_id": item_profile['movie_id'].values, "movie_idx": item_profile['movie_id'].values}

    user_embedding_model = Model(inputs=model.user_input, outputs=model.user_embedding)
    item_embedding_model = Model(inputs=model.item_input, outputs=model.item_embedding)

    user_embs = user_embedding_model.predict(test_user_model_input, batch_size=2 ** 12)
    item_embs = item_embedding_model.predict(all_item_model_input, batch_size=2 ** 12)

    print(user_embs.shape)
    print(item_embs.shape)

    # 5. [Optional] ANN search by faiss  and evaluate the result

    # test_true_label = {line[0]:[line[2]] for line in test_set}
    #
    # import numpy as np
    # import faiss
    # from tqdm import tqdm
    # from deepmatch.utils import recall_N
    #
    # index = faiss.IndexFlatIP(embedding_dim)
    # # faiss.normalize_L2(item_embs)
    # index.add(item_embs)
    # # faiss.normalize_L2(user_embs)
    # D, I = index.search(user_embs, 50)
    # s = []
    # hit = 0
    # for i, uid in tqdm(enumerate(test_user_model_input['user_id'])):
    #     try:
    #         pred = [item_profile['movie_id'].values[x] for x in I[i]]
    #         filter_item = None
    #         recall_score = recall_N(test_true_label[uid], pred, N=50)
    #         s.append(recall_score)
    #         if test_true_label[uid] in pred:
    #             hit += 1
    #     except:
    #         print(i)
    # print("recall", np.mean(s))
    # print("hr", hit / len(test_user_model_input['user_id']))

YoutubeDNN with sampled softmax

The MovieLens data has been used for personalized tag recommendation,which contains 668, 953 tag applications of users on movies. Here is a small fraction of data include only sparse field.

image

This example shows how to use YoutubeDNN to solve a matching task. You can get the demo data movielens_sample.txt and run the following codes.

import pandas as pd
from deepctr.inputs import SparseFeat, VarLenSparseFeat
from preprocess import gen_data_set, gen_model_input
from sklearn.preprocessing import LabelEncoder
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.models import Model

from deepmatch.models import *
from deepmatch.utils import sampledsoftmaxloss

if __name__ == "__main__":

    data = pd.read_csvdata = pd.read_csv("./movielens_sample.txt")
    sparse_features = ["movie_id", "user_id",
                       "gender", "age", "occupation", "zip", ]
    SEQ_LEN = 50
    negsample = 0

    # 1.Label Encoding for sparse features,and process sequence features with `gen_date_set` and `gen_model_input`

    features = ['user_id', 'movie_id', 'gender', 'age', 'occupation', 'zip']
    feature_max_idx = {}
    for feature in features:
        lbe = LabelEncoder()
        data[feature] = lbe.fit_transform(data[feature]) + 1
        feature_max_idx[feature] = data[feature].max() + 1

    user_profile = data[["user_id", "gender", "age", "occupation", "zip"]].drop_duplicates('user_id')

    item_profile = data[["movie_id"]].drop_duplicates('movie_id')

    user_profile.set_index("user_id", inplace=True)

    user_item_list = data.groupby("user_id")['movie_id'].apply(list)

    train_set, test_set = gen_data_set(data, negsample)

    train_model_input, train_label = gen_model_input(train_set, user_profile, SEQ_LEN)
    test_model_input, test_label = gen_model_input(test_set, user_profile, SEQ_LEN)

    # 2.count #unique features for each sparse field and generate feature config for sequence feature

    embedding_dim = 16

    user_feature_columns = [SparseFeat('user_id', feature_max_idx['user_id'], embedding_dim),
                            SparseFeat("gender", feature_max_idx['gender'], embedding_dim),
                            SparseFeat("age", feature_max_idx['age'], embedding_dim),
                            SparseFeat("occupation", feature_max_idx['occupation'], embedding_dim),
                            SparseFeat("zip", feature_max_idx['zip'], embedding_dim),
                            VarLenSparseFeat(SparseFeat('hist_movie_id', feature_max_idx['movie_id'], embedding_dim,
                                                        embedding_name="movie_id"), SEQ_LEN, 'mean', 'hist_len'),
                            ]

    item_feature_columns = [SparseFeat('movie_id', feature_max_idx['movie_id'], embedding_dim)]

    # 3.Define Model and train

    K.set_learning_phase(True)

    model = YoutubeDNN(user_feature_columns, item_feature_columns, num_sampled=5, user_dnn_hidden_units=(64, 16))
    # model = MIND(user_feature_columns,item_feature_columns,dynamic_k=True,p=1,k_max=2,num_sampled=5,user_dnn_hidden_units=(64,16),init_std=0.001)

    model.compile(optimizer="adagrad", loss=sampledsoftmaxloss)  # "binary_crossentropy")

    history = model.fit(train_model_input, train_label,  # train_label,
                        batch_size=256, epochs=1, verbose=1, validation_split=0.0, )

    # 4. Generate user features for testing and full item features for retrieval
    test_user_model_input = test_model_input
    all_item_model_input = {"movie_id": item_profile['movie_id'].values, "movie_idx": item_profile['movie_id'].values}

    user_embedding_model = Model(inputs=model.user_input, outputs=model.user_embedding)
    item_embedding_model = Model(inputs=model.item_input, outputs=model.item_embedding)

    user_embs = user_embedding_model.predict(test_user_model_input, batch_size=2 ** 12)
    # user_embs = user_embs[:, i, :]  i in [0,k_max) if MIND
    item_embs = item_embedding_model.predict(all_item_model_input, batch_size=2 ** 12)

    print(user_embs.shape)
    print(item_embs.shape)

    # 5. [Optional] ANN search by faiss  and evaluate the result

    # test_true_label = {line[0]:[line[2]] for line in test_set}
    #
    # import numpy as np
    # import faiss
    # from tqdm import tqdm
    # from deepmatch.utils import recall_N
    #
    # index = faiss.IndexFlatIP(embedding_dim)
    # # faiss.normalize_L2(item_embs)
    # index.add(item_embs)
    # # faiss.normalize_L2(user_embs)
    # D, I = index.search(user_embs, 50)
    # s = []
    # hit = 0
    # for i, uid in tqdm(enumerate(test_user_model_input['user_id'])):
    #     try:
    #         pred = [item_profile['movie_id'].values[x] for x in I[i]]
    #         filter_item = None
    #         recall_score = recall_N(test_true_label[uid], pred, N=50)
    #         s.append(recall_score)
    #         if test_true_label[uid] in pred:
    #             hit += 1
    #     except:
    #         print(i)
    # print("recall", np.mean(s))
    # print("hr", hit / len(test_user_model_input['user_id']))