diff --git a/caliGraph.py b/caliGraph.py
index fbd39d3..740ae05 100755
--- a/caliGraph.py
+++ b/caliGraph.py
@@ -294,7 +294,7 @@ def removeUselessReadBooks(G):
             else: # No unrated book in cousins
                 G.remove_node(n)
 
-def scoreOpinions(G, globMu, globStd, errorFac=0.5):
+def scoreOpinions(G, globMu, globStd, errorFac=-0.5):
     for n in list(G.nodes):
         node = G.nodes[n]
         feedbacks = []
@@ -308,7 +308,7 @@ def scoreOpinions(G, globMu, globStd, errorFac=0.5):
                 node['mean'], node['std'] = norm.fit(feedbacks)
                 node['se'] = globStd / math.sqrt(len(feedbacks))
                 ratio = len(feedbacks) / len(adjacens)
-                node['score'] = node['mean'] - errorFac * \
+                node['score'] = node['mean'] + errorFac * \
                     node['se']*(6/7 + (1-ratio)/7) + 0.01 * \
                     (node['t'] == 'recommender') \
                     - 0.5 / len(feedbacks)**2
@@ -316,12 +316,10 @@ def scoreOpinions(G, globMu, globStd, errorFac=0.5):
             else:
                 node['score'] = None
 
-
-def scoreUnread(G, globMu, globStd, errorFac=0.6):
+def scoreUnread(G, globMu, globStd, errorFac=-0.6):
     for n in list(G.nodes):
-        feedbacks = []
-        deepFeedbacks = [globMu - globStd*0.5]
-        deepLen = 1
+        feedbacks = [globMu]
+        wheights = [getWheightForType('mu')]
         node = G.nodes[n]
         if node['t'] == 'book':
             if node['rating'] == None:
@@ -329,33 +327,27 @@ def scoreUnread(G, globMu, globStd, errorFac=0.6):
                 for adj in adjacens:
                     adjNode = G.nodes[adj]
                     if 'score' in adjNode and adjNode['score'] != None:
-                        if adjNode['t'] == 'tag':
-                            w = int(10/(len(G.adj[adj])))
-                        elif adjNode['t'] == 'topList':
-                            w = int(G[n][adj]['wheight']*5)
-                        else:
-                            w = 10
-                        feedbacks.append(adjNode['score'])
+                        w = getWheightForType(adjNode['t'], G[n][adj]['wheight'] if 'wheight' in G[n][adj] else None)
                         for fb in adjNode['feedbacks']:
-                            for i in range(w):
-                                deepFeedbacks.append(fb)
-                            deepLen += w
+                            feedbacks.append(fb)
+                            wheights.append(w)
                 if len(feedbacks):
-                    node['mean'], node['std'] = norm.fit(deepFeedbacks)
-                    node['mean2'], node['std2'] = norm.fit(feedbacks)
-                    if deepLen:
-                        node['se'] = globStd / math.sqrt(deepLen)
-                    # - errorFac*node['se']
-                        node['score'] = (
-                            (node['mean'] - errorFac*node['se'])*3 + node['mean2']*2)/5
-                    else:
-                        node['score'] = globMu - errorFac*globStd
-                    if 'series' in node:
-                        if node['series_index'] == 1.0:
-                            node['score'] += 0.000000001
+                    node['meanUnweighted'], node['std'] = norm.fit(feedbacks)
+                    node['mean'] = sum([fb*w for fb, w in zip(feedbacks, wheights)])/len(feedbacks)
+                    node['se'] = globStd / math.sqrt(len(feedbacks))
+                    node['score'] = node['mean'] + errorFac*node['se']
                 else:
-                    node['score'] = None
+                    node['score'] = globMu + errorFac*globStd + len(feedbacks)*0.0000000001
+                if 'series' in node:
+                    if node['series_index'] == 1.0:
+                        node['score'] += 0.000000001
 
+# TODO: Make this neural and train it
+def getWheightForType(nodeType, edgeWheight=None):
+    if nodeType == 'topList':
+        return edgeWheight*0.5
+    else:
+        return 1.0
 
 def printBestList(G, num=-1):
     bestlist = []