def rerank_results(self, results, user_vector, user_gender, user_location,
user_sentiment):
"""
reranks the results of a query by using the similarity between the user thematic vector and the vector from the tweets
:param results: the documents resulting from a query
:param user_vector: the thematic vector of a user
:param user_gender: the gender of a user
:param user_location: the location of a user
:param user_sentiment: the sentiment of a user
:return: the reranked list of documents
"""
reranked = []
user_vec = ProfileOneHotEncoder.add_info_to_vec(
user_vector, user_gender, user_location,
user_sentiment).reshape(1, -1)
for i in range(len(results)):
doc_infos = Tweet.load(int(results[i]['TweetID']))
if doc_infos is None:
reranked.append({'doc': results[i], 'sim': 0.})
else:
doc_vector = ProfileOneHotEncoder.add_info_to_vec(
doc_infos.vector, doc_infos.gender, doc_infos.country,
doc_infos.sentiment).reshape(1, -1)
sim = cosine_similarity(user_vec, doc_vector)
reranked.append({'doc': doc_infos, 'sim': sim[0][0]})
reranked = sorted(reranked, key=lambda k: k['sim'], reverse=True)
return [x['doc'] for x in reranked]
def mark_view():
user = User.load(user_name=session.get('username'))
tweet = Tweet.load(int(request.form.get('tweet_id')))
state = ''
if tweet.is_faved(user):
state = 'removed'
# user.remove_favorite(tweet)
else:
view = Favorite(user_id=user.id, tweet_id=tweet.id)
# user.update_profile(np.array(tweet.vector))
DB.get_instance().add(view)
state = 'added'
DB.get_instance().commit()
return state
def link_tweets(self, results):
return [Tweet.load(r['TweetID']) for r in results]