def generate_all_terms_tfidf():
global user_tfidf_weights
axipath = os.path.expanduser("~/.app-recommender/axi_desktopapps/")
axi_index = xapian.Database(axipath)
dpkg_output = commands.getoutput('apt-mark showmanual')
pkgs = [pkg for pkg in dpkg_output.splitlines()
if not pkg.startswith('lib')]
docs = data.axi_search_pkgs(axi_index, pkgs)
tags_weights = data.tfidf_weighting(axi_index, docs,
FilterTag(0), time_context=0)
description_weights = (data.tfidf_weighting(axi_index, docs,
FilterDescription(), time_context=0))
user_tfidf_weights = dict(tags_weights + description_weights)
def tfidf_profile(self,items_repository,size,content_filter):
"""
Return the most relevant tags for the user list of packages based on
the sublinear tfidf weight of packages' tags.
"""
docs = data.axi_search_pkgs(items_repository,self.pkg_profile)
#weights = data.tfidf_plus(items_repository,docs,content_filter)
weights = data.tfidf_weighting(items_repository,docs,content_filter)
# Eliminate duplicated stemmed term
profile = self._eliminate_duplicated([w[0] for w in weights],size)
return profile
def run(self, rec, user, recommendation_size):
"""
Perform recommendation strategy.
"""
neighborhood = self.get_neighborhood(user, rec)
weights = data.tfidf_weighting(rec.users_repository, neighborhood, PkgExpandDecider(user.items()))
profile = [w[0] for w in weights][: rec.cfg.profile_size]
result = ContentBased("tag", rec.cfg.profile_size).get_sugestion_from_profile(
rec, user, profile, recommendation_size
)
return result
def run(self, rec, user, recommendation_size):
"""
Perform recommendation strategy.
"""
neighborhood = self.get_neighborhood(user, rec)
weights = data.tfidf_weighting(rec.users_repository, neighborhood,
PkgExpandDecider(user.items()))
profile = [w[0] for w in weights][:rec.cfg.profile_size]
result = ContentBased("tag", rec.cfg.profile_size)
result = result.get_sugestion_from_profile(rec, user, profile,
recommendation_size)
return result
def run(self, rec, user, recommendation_size):
"""
Perform recommendation strategy.
"""
neighborhood = self.get_neighborhood(user, rec)
weights = data.tfidf_weighting(rec.users_repository, neighborhood, PkgExpandDecider(user.items()))
item_score = {}
ranking = []
for pkg in weights[:recommendation_size]:
package = pkg[0].lstrip("XP")
item_score[package] = pkg[1]
ranking.append(package)
result = recommender.RecommendationResult(item_score, ranking)
return result
def run(self, rec, user, recommendation_size):
"""
Perform recommendation strategy.
"""
neighborhood = self.get_neighborhood(user, rec)
weights = data.tfidf_weighting(rec.users_repository, neighborhood,
PkgExpandDecider(user.items()))
item_score = {}
ranking = []
for pkg in weights[:recommendation_size]:
package = pkg[0].lstrip("XP")
item_score[package] = pkg[1]
ranking.append(package)
result = recommender.RecommendationResult(item_score, ranking)
return result
