def update(self, user_ix, item_i, item_j):
''' u * k
C = U s V
D =
utility = rate_ui , price_ui
utility = alpha * rate - beta * price
- utility_2
rate_ui
object = alpha_uc * (rate_1 - rate_2) + beta_uc * (price_1 - price_2)
model_score_i = self.model_score_without_log(alpha, beta, gamma, rate_i, price_i)
model_score_j = self.model_score_without_log(alpha, beta, gamma, rate_j, price_j)
new_alpha = alpha - self.lambda_iter * ((rate_i * model_score_j - rate_j * model_score_i) / (model_score_j ** 2) - 2 * self.lambda_regular * alpha)
new_beta = beta - self.lambda_iter * ((price_i**2 * model_score_j - price_j * model_score_i) / (model_score_j ** 2) - 2 * self.lambda_regular * beta)
new_gamma = gamma - self.lambda_iter * ((model_score_j - model_score_i) / (model_score_j ** 2) - 2 * self.lambda_regular * gamma)
'''
alpha = self.alpha[user_ix]
beta = self.beta[user_ix]
rate_i = self.mf_rate[user_ix][item_i]
rate_j = self.mf_rate[user_ix][item_j]
prob_i = MyGaussian.get_prob_from_gaussian(self.dataModel.getUserByUid(user_ix), 'phones', self.personal_item_price[user_ix][item_i], self.param_mu, self.param_sigm)
prob_j = MyGaussian.get_prob_from_gaussian(self.dataModel.getUserByUid(user_ix), 'phones', self.personal_item_price[user_ix][item_j], self.param_mu, self.param_sigm)
new_alpha = alpha + self.lambda_iter * (rate_i - rate_j) - 2 * self.lambda_regular * alpha**2
new_beta = beta + self.lambda_iter * (np.exp(prob_i) - np.exp(prob_j)) - 2 * self.lambda_regular * beta**2
return new_alpha, new_beta
def model_score(self, user_ix, item_ix):
alpha = self.alpha[user_ix]
beta = self.beta[user_ix]
gamma = self.gamma[user_ix]
rate = self.mf_rate[user_ix, item_ix]
price = self.personal_item_price[user_ix][item_ix]
prob = MyGaussian.get_prob_from_gaussian(self.dataModel.getUserByUid(user_ix), 'phones', self.personal_item_price[user_ix][item_ix], self.param_mu, self.param_sigm)
result = alpha * rate + beta * np.exp(prob) + gamma
return result
def update(self, user_ix, item_i, item_j):
''' u * k
C = U s V
D =
utility = rate_ui , price_ui
utility = alpha * rate - beta * price
- utility_2
rate_ui
object = alpha_uc * (rate_1 - rate_2) + beta_uc * (price_1 - price_2)
model_score_i = self.model_score_without_log(alpha, beta, gamma, rate_i, price_i)
model_score_j = self.model_score_without_log(alpha, beta, gamma, rate_j, price_j)
new_alpha = alpha - self.lambda_iter * ((rate_i * model_score_j - rate_j * model_score_i) / (model_score_j ** 2) - 2 * self.lambda_regular * alpha)
new_beta = beta - self.lambda_iter * ((price_i**2 * model_score_j - price_j * model_score_i) / (model_score_j ** 2) - 2 * self.lambda_regular * beta)
new_gamma = gamma - self.lambda_iter * ((model_score_j - model_score_i) / (model_score_j ** 2) - 2 * self.lambda_regular * gamma)
'''
alpha = self.alpha[user_ix]
beta = self.beta[user_ix]
rate_i = self.mf_rate[user_ix][item_i]
rate_j = self.mf_rate[user_ix][item_j]
prob_i = MyGaussian.get_prob_from_gaussian(
self.dataModel.getUserByUid(user_ix), 'phones',
self.personal_item_price[user_ix][item_i], self.param_mu,
self.param_sigm)
prob_j = MyGaussian.get_prob_from_gaussian(
self.dataModel.getUserByUid(user_ix), 'phones',
self.personal_item_price[user_ix][item_j], self.param_mu,
self.param_sigm)
new_alpha = alpha + self.lambda_iter * (
rate_i - rate_j) - 2 * self.lambda_regular * alpha**2
new_beta = beta + self.lambda_iter * (np.exp(prob_i) - np.exp(
prob_j)) - 2 * self.lambda_regular * beta**2
return new_alpha, new_beta
def model_score(self, user_ix, item_ix):
alpha = self.alpha[user_ix]
beta = self.beta[user_ix]
gamma = self.gamma[user_ix]
rate = self.mf_rate[user_ix, item_ix]
price = self.personal_item_price[user_ix][item_ix]
prob = MyGaussian.get_prob_from_gaussian(
self.dataModel.getUserByUid(user_ix), 'phones',
self.personal_item_price[user_ix][item_ix], self.param_mu,
self.param_sigm)
result = alpha * rate + beta * np.exp(prob) + gamma
return result
def _construct_mention_feature_apply(dataModel, origin_user_price_feature, row,
user_sigm):
price_ix = dataModel.getPidByPriceIx(row['price_ix'])
feature = [dataModel.getFidByFeature(i[0]) for i in eval(row['feature'])]
min_ix = int(price_ix - user_sigm)
max_ix = int(price_ix + user_sigm)
if min_ix < 0:
min_ix = 0
if max_ix > dataModel.getPriceIxNum():
max_ix = dataModel.getPriceIxNum()
for i in xrange(min_ix, max_ix):
origin_user_price_feature[i][feature] = origin_user_price_feature[i][
feature] + MyGaussian.get_prob_from_gaussian(
i, price_ix, user_sigm)
def _construct_mention_feature_apply(dataModel, origin_user_price_feature, row, user_sigm):
price_ix = dataModel.getPidByPriceIx(row['price_ix'])
feature = [dataModel.getFidByFeature(i[0]) for i in eval(row['feature'])]
min_ix = int(price_ix - user_sigm)
max_ix = int(price_ix + user_sigm)
if min_ix < 0:
min_ix = 0
if max_ix > dataModel.getPriceIxNum():
max_ix = dataModel.getPriceIxNum()
for i in xrange(min_ix, max_ix):
origin_user_price_feature[i][feature] = origin_user_price_feature[i][feature] + MyGaussian.get_prob_from_gaussian(i, price_ix, user_sigm)
