model_1

def get_model_1(max_work, max_user):
    dim_embedddings = 30
    bias = 3
    # inputs
    w_inputs = Input(shape=(1,), dtype='int32')
    w = Embedding(max_work+1, dim_embedddings, name="work")(w_inputs)

    # context
    u_inputs = Input(shape=(1,), dtype='int32')
    u = Embedding(max_user+1, dim_embedddings, name="user")(u_inputs)
    o = multiply([w, u])
    o = Dropout(0.5)(o)
    o = Flatten()(o)
    o = Dense(1)(o)

    rec_model = Model(inputs=[w_inputs, u_inputs], outputs=o)
    #rec_model.summary()
    rec_model.compile(loss='mae', optimizer='adam', metrics=["mae"])

    return rec_model