我尝试建立基于内容的过滤模型，但出现TypeError“'numpy.float64'对象不可迭代”。我是Python的新手，如果您给我一些建议，我应该编辑些什么，将不胜感激。
对于其他数据集，此代码运行良好，但与此相同，这可能是一个问题吗？
def get_item_profile(item_id):
idx = item_ids.index(item_id)
item_profile = tfidf_matrix[idx:idx+1]
return item_profile
def get_item_profiles(ids):
item_profiles_list = [get_item_profile(x) for x in ids]
item_profiles = scipy.sparse.vstack(item_profiles_list)
return item_profiles
def build_users_profile(person_id, interactions_indexed_df):
interactions_person_df = interactions_indexed_df.loc[person_id]
user_item_profiles = get_item_profiles(interactions_person_df['contentId'])
user_item_strengths = np.array(interactions_person_df['eventStrength']).reshape(-1,1)
#Weighted average of item profiles by the interactions strength
user_item_strengths_weighted_avg = np.sum(user_item_profiles.multiply(user_item_strengths), axis=0) / np.sum(user_item_strengths)
user_profile_norm = sklearn.preprocessing.normalize(user_item_strengths_weighted_avg)
return user_profile_norm
def build_users_profiles():
interactions_indexed_df = interactions_full_df[interactions_full_df['contentId'] \
.isin(articles_df['contentId'])].set_index('personId')
user_profiles = {}
for person_id in interactions_indexed_df.index.unique():
user_profiles[person_id] = build_users_profile(person_id, interactions_indexed_df)
return user_profiles
错误信息：
--------------------------------------------------------------------------- …