Better recommender scores
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@ -398,7 +398,7 @@ def scoreOpinions(G, globMu, globStd):
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node['mean'], node['std'] = norm.fit(feedbacks)
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node['mean'], node['std'] = norm.fit(feedbacks)
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node['se'] = globStd / math.sqrt(len(feedbacks))
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node['se'] = globStd / math.sqrt(len(feedbacks))
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ratio = len(feedbacks) / len(adjacens)
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ratio = len(feedbacks) / len(adjacens)
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node['score'] = node['mean']
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node['score'] = node['mean'] + globStd/3 - node['se']
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node['feedbacks'] = feedbacks
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node['feedbacks'] = feedbacks
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else:
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else:
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node['score'] = None
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node['score'] = None
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@ -1186,7 +1186,7 @@ def findNewBooks(G, books, mu, num=-1, minRecSco=5):
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ses.append(min(ses))
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ses.append(min(ses))
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scores.append(mu)
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scores.append(mu)
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node['fake_se'] = sum(ses)/(len(ses)**1.2) + 0.5 + 0.5 * (len(scores)==2) # This is not how SE works. DILLIGAF?
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node['fake_se'] = sum(ses)/(len(ses)**1.2) + 0.5 + 0.5 * (len(scores)==2) # This is not how SE works. DILLIGAF?
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node['score'] = sum(scores)/len(scores)*1.2 - node['fake_se']*2 + 0.5 - 0.1/math.sqrt(len(scores))
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node['score'] = sum(scores)/len(scores)*1.2 - node['fake_se']*1.6 + 0.5 - 0.1/math.sqrt(len(scores))
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if len(scores)==2:
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if len(scores)==2:
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node['score']*=0.80
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node['score']*=0.80
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node['value'] = 20 + 5 * float(node['score'])
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node['value'] = 20 + 5 * float(node['score'])
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