def get_hated_sim(uid, eid):
u_his = user_event[user_event.user_id == uid]
hated = u_his.at[u_his.index[0], 'declined'].split()
score = []
feature = [0, 0, 0]
if (len(hated) == 0):
return feature
# get tf-idf vector of eid
e = event_tfidf[event_tfidf.event_id == eid]
vec_e = list(e.ix[e.index[0], 1:])
# get tf-idf vector of hated events
h_e = event_tfidf[event_tfidf.event_id.isin(hated)]
for i in range(len(h_e)):
vec_he = list(h_e.ix[h_e.index[i], 1:])
score.append(formula.cos_sim(vec_e, vec_he))
feature[0] = min(score)
feature[1] = max(score)
feature[2] = sum(score) / len(hated)
return feature
def get_interested_sim(uid, eid):
u_his = user_event[user_event.user_id == uid]
interested = u_his.at[u_his.index[0], 'attend'].split() + u_his.at[u_his.index[0], 'maybe'].split()
score = []
feature = [0, 0, 0]
if (len(interested) == 0):
return feature
# get tf-idf vector of eid
e = event_tfidf[event_tfidf.event_id == eid]
vec_e = list(e.ix[e.index[0], 1:])
# get tf-idf vector of interested events
i_e = event_tfidf[event_tfidf.event_id.isin(interested)]
for i in range(len(i_e.index)):
vec_ie = list(i_e.ix[i_e.index[i], 1:])
score.append(formula.cos_sim(vec_e, vec_ie))
feature[0] = min(score)
feature[1] = max(score)
feature[2] = sum(score) / len(interested)
return feature
