def get_recommendation(current_user):
cursor = db.user.find()
for doc in cursor:
user = doc['_id']
if str(user) == str(current_user):
get_nearest_neighbours(user_vector[str(user)])
def get_recommendation(current_user): cursor = db.user.find() for doc in cursor: user = doc['_id'] if str(user) == str(current_user): get_nearest_neighbours(user_vector[str(user)])
def get_recommendation(current_user):
cursor = db.user.find()
for doc in cursor:
user = doc['_id']
if user == current_user:
Good = doc['rates']['good']
Avg= doc['rates']['avg']
Poor = doc['rates']['poor']
list_of_weight = [[Good,8], [Avg,6], [Poor,-1]]
with open("current_user_info.txt", "w") as f:
f.write("\n"+str(user)+">")
for i in list_of_weight:
for j in i[0]:
f.write(str(j)+":"+str(i[1])+"\t")
f.write("\n")
with open("current_user_info.txt","r") as f:
for line in f.readlines():
line = line.strip()
data = line.split(">")
if len(data) > 1:
user = data[0].strip()
list_of_vid = data[1]
else:
user = data[0].strip()
list_of_vid = []
list_of_watched_videos = []
if list_of_vid:
vid_rat_pair = list_of_vid.split("\t")
for pair in vid_rat_pair:
list_of_watched_videos.append((pair.split(":")[0],pair.split(":")[1]))
list_of_videos.add(pair.split(":")[0])
current_user_rating[user] = list_of_watched_videos
for user in current_user_rating:
current_user_vector[user] = [0 for i in range(len(list_of_videos))]
for vid in range(len(list_of_videos)):
for v_id in user:
if list_of_videos[vid] == v_id[0]:
current_user_vector[user][vid] = v_id[1]
current_user_vector[user] = np.array(user_vector[user],dtype=float)
return get_nearest_neighbours(current_user_vector[user])
