class FeatureWeightedLinearStacking(base_recommender):
def __init__(self):
self.cb = ContentBasedRecs()
self.cf = NeighborhoodBasedRecs()
self.wcb1 = Decimal(0.65221204)
self.wcb2 = Decimal(-0.14638855)
self.wcf1 = Decimal(-0.0062952)
self.wcf2 = Decimal(0.09139193)
def fun1(self):
return Decimal(1.0)
def fun2(self, user_id):
count = Rating.objects.filter(user_id=user_id).count()
if count > 3.0:
return Decimal(1.0)
return Decimal(0.0)
def recommend_items(self, user_id, num=6):
cb_recs = self.cb.recommend_items(user_id, num * 5)
cf_recs = self.cf.recommend_items(user_id, num * 5)
combined_recs = dict()
for rec in cb_recs:
movie_id = rec[0]
pred = rec[1]['prediction']
combined_recs[movie_id] = {'cb': pred}
for rec in cf_recs:
movie_id = rec[0]
pred = rec[1]['prediction']
if movie_id in combined_recs.keys():
combined_recs[movie_id]['cf'] = pred
else:
combined_recs[movie_id] = {'cf': pred}
fwls_preds = dict()
for key, recs in combined_recs.items():
if 'cb' not in recs.keys():
recs['cb'] = self.cb.predict_score(user_id, key)
if 'cf' not in recs.keys():
recs['cf'] = self.cf.predict_score(user_id, key)
pred = self.prediction(recs['cb'], recs['cf'], user_id)
fwls_preds[key] = {'prediction': pred}
sorted_items = sorted(fwls_preds.items(), key=lambda item: -float(item[1]['prediction']))[:num]
return sorted_items
def predict_score(self, user_id, item_id):
p_cb = self.cb.predict_score(user_id, item_id)
p_cf = self.cf.predict_score(user_id, item_id)
self.prediction(p_cb, p_cf, user_id)
def prediction(self, p_cb, p_cf, user_id):
p = (self.wcb1 * self.fun1() * p_cb +
self.wcb2 * self.fun2(user_id) * p_cb +
self.wcf1 * self.fun1() * p_cf +
self.wcf2 * self.fun2(user_id) * p_cf)
return p
class FeatureWeightedLinearStacking(base_recommender):
def __init__(self):
self.cb = ContentBasedRecs()
self.cf = NeighborhoodBasedRecs()
self.wcb1 = Decimal(0.65221204)
self.wcb2 = Decimal(-0.14638855)
self.wcf1 = Decimal(-0.0062952)
self.wcf2 = Decimal(0.09139193)
self.intercept = Decimal(0)
def fun1(self):
return Decimal(1.0)
def fun2(self, user_id):
count = Rating.objects.filter(user_id=user_id).count()
if count > 3.0:
return Decimal(1.0)
return Decimal(0.0)
def set_save_path(self, save_path):
with open(save_path + 'fwls_parameters.data', 'rb') as ub_file:
parameters = pickle.load(ub_file)
self.wcb1 = Decimal(parameters['cb1'])
self.wcb2 = Decimal(parameters['cb2'])
self.wcf1 = Decimal(parameters['cb1'])
self.wcf2 = Decimal(parameters['cf2'])
self.intercept = Decimal(parameters['intercept'])
def recommend_items_by_ratings(self, user_id, active_user_items, num=6):
cb_recs = self.cb.recommend_items_by_ratings(user_id,
active_user_items,
num * 5)
cf_recs = self.cf.recommend_items_by_ratings(user_id,
active_user_items,
num * 5)
return self.merge_predictions(user_id, cb_recs, cf_recs, num)
def recommend_items(self, user_id, num=6):
cb_recs = self.cb.recommend_items(user_id, num * 5)
cf_recs = self.cf.recommend_items(user_id, num * 5)
return self.merge_predictions(user_id, cb_recs, cf_recs, num)
def merge_predictions(self, user_id, cb_recs, cf_recs, num):
combined_recs = dict()
for rec in cb_recs:
movie_id = rec[0]
pred = rec[1]['prediction']
combined_recs[movie_id] = {'cb': pred}
for rec in cf_recs:
movie_id = rec[0]
pred = rec[1]['prediction']
if movie_id in combined_recs.keys():
combined_recs[movie_id]['cf'] = pred
else:
combined_recs[movie_id] = {'cf': pred}
fwls_preds = dict()
for key, recs in combined_recs.items():
if 'cb' not in recs.keys():
recs['cb'] = self.cb.predict_score(user_id, key)
if 'cf' not in recs.keys():
recs['cf'] = self.cf.predict_score(user_id, key)
pred = self.prediction(recs['cb'], recs['cf'], user_id)
fwls_preds[key] = {'prediction': pred}
sorted_items = sorted(
fwls_preds.items(),
key=lambda item: -float(item[1]['prediction']))[:num]
return sorted_items
def predict_score(self, user_id, item_id):
p_cb = self.cb.predict_score(user_id, item_id)
p_cf = self.cf.predict_score(user_id, item_id)
self.prediction(p_cb, p_cf, user_id)
def prediction(self, p_cb, p_cf, user_id):
p = (self.wcb1 * self.fun1() * p_cb +
self.wcb2 * self.fun2(user_id) * p_cb +
self.wcf1 * self.fun1() * p_cf +
self.wcf2 * self.fun2(user_id) * p_cf)
return p + self.intercept
