def contentBasedFiltering(self, key, n=3): '''Return list of n top match scores along with other keys''' dataset = self.dataset scores = [] for other_key in dataset: if other_key == key: continue # Fetching common inner keys to calculate similarity score common_inner_keys = self.fetchCommonInnerKeys(key, other_key) # If there is no common inner key, skip this other keys if len(common_inner_keys) == 0: continue x = [dataset[key][inner_key] for inner_key in common_inner_keys] y = [dataset[other_key][inner_key] for inner_key in common_inner_keys] # Appending the similarity score to a list sim = Similarity() scores.append((sim.pearson(x, y), other_key)) # Sorting the list so the highest score appear at the top scores.sort() scores.reverse() return scores[0:n]
def contentBasedFiltering(self, key, n=3):
'''Return list of n top match scores along with other keys'''
dataset = self.dataset
scores = []
for other_key in dataset:
if other_key == key:
continue
# Fetching common inner keys to calculate similarity score
common_inner_keys = self.fetchCommonInnerKeys(key, other_key)
# If there is no common inner key, skip this other keys
if len(common_inner_keys) == 0:
continue
x = [dataset[key][inner_key] for inner_key in common_inner_keys]
y = [
dataset[other_key][inner_key]
for inner_key in common_inner_keys
]
# Appending the similarity score to a list
sim = Similarity()
scores.append((sim.pearson(x, y), other_key))
# Sorting the list so the highest score appear at the top
scores.sort()
scores.reverse()
return scores[0:n]
def collaborativeRecommendation(self, key, n=3):
'''Return list of n top match scores along with inner keys'''
dataset = self.dataset
weighted_inner_values = {}
total_scores = {}
for other_key in dataset:
if other_key == key:
continue
# Fetching common inner keys to calculate similarity score
common_inner_keys = self.fetchCommonInnerKeys(key, other_key)
# If there is no common inner key, skip this other keys
if len(common_inner_keys) == 0:
continue
x = [dataset[key][inner_key] for inner_key in common_inner_keys]
y = [
dataset[other_key][inner_key]
for inner_key in common_inner_keys
]
# Finding similarity score
sim = Similarity()
score = sim.pearson(x, y)
# Ignoring scores of zero or below
if score <= 0:
continue
for inner_key in dataset[other_key]:
if inner_key not in dataset[key] or dataset[key][
inner_key] == 0:
# Weighted sum of value times similarity score
weighted_inner_values.setdefault(inner_key, 0)
weighted_inner_values[
inner_key] += score * dataset[other_key][inner_key]
# Sum of similarity score
total_scores.setdefault(inner_key, 0)
total_scores[inner_key] += score
scores = [(weighted_inner_values[inner_key] / total_scores[inner_key],
inner_key) for inner_key in weighted_inner_values]
# Sorting the list so that highest score appear at the top
scores.sort()
scores.reverse()
return scores[0:n]
def collaborativeRecommendation(self, key, n=3):
'''Return list of n top match scores along with inner keys'''
dataset = self.dataset
weighted_inner_values = {}
total_scores = {}
for other_key in dataset:
if other_key == key:
continue
# Fetching common inner keys to calculate similarity score
common_inner_keys = self.fetchCommonInnerKeys(key, other_key)
# If there is no common inner key, skip this other keys
if len(common_inner_keys) == 0:
continue
x = [dataset[key][inner_key] for inner_key in common_inner_keys]
y = [dataset[other_key][inner_key] for inner_key in common_inner_keys]
# Finding similarity score
sim = Similarity()
score = sim.pearson(x, y)
# Ignoring scores of zero or below
if score <= 0:
continue
for inner_key in dataset[other_key]:
if inner_key not in dataset[key] or dataset[key][inner_key] == 0:
# Weighted sum of value times similarity score
weighted_inner_values.setdefault(inner_key, 0)
weighted_inner_values[inner_key] += score * dataset[other_key][inner_key]
# Sum of similarity score
total_scores.setdefault(inner_key, 0)
total_scores[inner_key] += score
scores = [(weighted_inner_values[inner_key]/total_scores[inner_key], inner_key) for inner_key in weighted_inner_values]
# Sorting the list so that highest score appear at the top
scores.sort()
scores.reverse()
return scores[0:n]
