def build_mlp(user_num, item_num):
input = Input(shape=[2])
user_emb_mlp = LatentFactorMapper(feat_column_id=0,
id_num=user_num,
embedding_dim=64)(input)
item_emb_mlp = LatentFactorMapper(feat_column_id=1,
id_num=user_num,
embedding_dim=64)(input)
output = MLPInteraction()([user_emb_mlp, item_emb_mlp])
output = RatingPredictionOptimizer()(output)
model = RPRecommender(inputs=input, outputs=output)
return model
def build_gmf(user_num, item_num):
input = Input(shape=[2])
user_emb = LatentFactorMapper(column_id=0,
num_of_entities=user_num,
embedding_dim=64)(input)
item_emb = LatentFactorMapper(column_id=1,
num_of_entities=item_num,
embedding_dim=64)(input)
output = InnerProductInteraction()([user_emb, item_emb])
output = RatingPredictionOptimizer()(output)
model = RPRecommender(inputs=input, outputs=output)
return model
def build_gmf(user_num, item_num):
input = Input(shape=[2])
user_emb = LatentFactorMapper(feat_column_id=0,
id_num=user_num,
embedding_dim=64)(input)
item_emb = LatentFactorMapper(feat_column_id=1,
id_num=item_num,
embedding_dim=64)(input)
output = ElementwiseInteraction(elementwise_type="innerporduct")(
[user_emb, item_emb])
output = RatingPredictionOptimizer()(output)
model = RPRecommender(inputs=input, outputs=output)
return model
def build_autorec(user_num, item_num):
input = Input(shape=[2])
user_emb_1 = LatentFactorMapper(column_id=0,
num_of_entities=user_num,
embedding_dim=64)(input)
item_emb_1 = LatentFactorMapper(column_id=1,
num_of_entities=item_num,
embedding_dim=64)(input)
user_emb_2 = LatentFactorMapper(column_id=0,
num_of_entities=user_num,
embedding_dim=64)(input)
item_emb_2 = LatentFactorMapper(column_id=1,
num_of_entities=item_num,
embedding_dim=64)(input)
output = HyperInteraction()(
[user_emb_1, item_emb_1, user_emb_2, item_emb_2])
output = RatingPredictionOptimizer()(output)
model = RPRecommender(inputs=input, outputs=output)
return model
def build_neumf(user_num, item_num):
input = Input(shape=[2])
user_emb_gmf = LatentFactorMapper(column_id=0,
num_of_entities=user_num,
embedding_dim=64)(input)
item_emb_gmf = LatentFactorMapper(column_id=1,
num_of_entities=item_num,
embedding_dim=64)(input)
innerproduct_output = InnerProductInteraction()(
[user_emb_gmf, item_emb_gmf])
user_emb_mlp = LatentFactorMapper(column_id=0,
num_of_entities=user_num,
embedding_dim=64)(input)
item_emb_mlp = LatentFactorMapper(column_id=1,
num_of_entities=item_num,
embedding_dim=64)(input)
mlp_output = MLPInteraction()([user_emb_mlp, item_emb_mlp])
output = RatingPredictionOptimizer()([innerproduct_output, mlp_output])
model = RPRecommender(inputs=input, outputs=output)
return model
num_of_entities=item_num,
embedding_dim=64)(input)
user_emb_mlp = LatentFactorMapper(column_id=0,
num_of_entities=user_num,
embedding_dim=64)(input)
item_emb_mlp = LatentFactorMapper(column_id=1,
num_of_entities=item_num,
embedding_dim=64)(input)
# Step 2.2: Setup interactors to handle models
innerproduct_output = InnerProductInteraction()([user_emb_gmf, item_emb_gmf])
mlp_output = MLPInteraction()([user_emb_mlp, item_emb_mlp])
# Step 2.3: Setup optimizer to handle the target task
output = RatingPredictionOptimizer()([innerproduct_output, mlp_output])
model = RPRecommender(inputs=input, outputs=output)
# Step 3: Build the searcher, which provides search algorithm
searcher = Search(model=model,
tuner='greedy', # random, greedy
tuner_params={"max_trials": 5, 'overwrite': True}
)
# Step 4: Use the searcher to search the recommender
searcher.search(x=[train_X_categorical],
y=train_y,
x_val=[val_X_categorical],
y_val=val_y,
objective='val_mse',
batch_size=1024,
epochs=1,
