def obtain_history_decay_factor(self, product_id):
""" Produces a history decay factor according to some mathematical function of
the number of previous recommendations of a certain product to a certain user.
:param product_id: The id of the product.
"""
if self.history_decay_function_name is None:
return 1
if self.user_impressions_summary is not None:
latest_impressions_count = self.user_impressions_summary.get(product_id, (0, None))[0]
else:
latest_impressions_count = 0
result = 1
if self.history_decay_function_name == "linear":
ttl = self.history_decay_linear_function_ttl
result = df.linear(latest_impressions_count, ttl)
elif self.history_decay_function_name == "rational":
result = df.rational(latest_impressions_count)
elif self.history_decay_function_name == "exponential":
halflife = self.history_decay_exponential_function_halflife
result = df.exponential(latest_impressions_count, halflife)
elif self.history_decay_function_name == "step":
ttl = self.history_decay_step_function_ttl
result = df.step(latest_impressions_count, 1, -1, ttl)
return result
def obtain_product_age_decay_factor(self, product_date, present_date):
""" Produces an age decay factor according to some mathematical function of the
number of units of time (days, weeks) since the product was added to the system.
:param product_date: The date of the product.
:param present_date: The current date.
"""
if self.product_age_decay_function_name is None:
return 1
product_age_in_days = None
if product_date is not None:
try:
utc_product_date = pytz.utc.localize(product_date)
except Exception:
utc_product_date = product_date
product_age = present_date - utc_product_date
product_age_in_days = product_age.days
result = 1
if self.product_age_decay_function_name == "linear":
ttl = self.product_age_decay_linear_function_ttl
result = df.linear(product_age_in_days, ttl)
elif self.product_age_decay_function_name == "rational":
result = df.rational(product_age_in_days)
elif self.product_age_decay_function_name == "exponential":
halflife = self.product_age_decay_exponential_function_halflife
result = df.exponential(product_age_in_days, halflife)
elif self.product_age_decay_function_name == "step":
ttl = self.product_age_decay_step_function_ttl
result = df.step(product_age_in_days, 1, -1, ttl)
return result
