Topic0japanese
Topic1mexican
Topic2brunch
Topic3bar/
atmosphere
Topic4pizza
Topic 5compliment
sushi tacos breakfast great pizza great
roll mexican coffee beer crust best
pita salsa eggs happyhour wings love
tuna burrito bacon bar thin good
salmon chips pancakes drinks pepperoni like
Topic6indian
Topic7asian
Topic8fastfood
Topic9sweets
Topic10bbq
Topic 11service(bad)
Indian Thai burger bagels cheese service
buffet Pho fries cheese bbq didn’t
masala Chinese potato best sauce never
naan soup onion ring smoothies chicken even
bianco curry dog Iove ribs back
LDA (Latent Dirichlet Allocation) 12 topics
Good restaurant: average star>3.5 Bad restaurant: average star<=3.5
Classification : Weight for each topic
Classifier Linear SVM Logistic Regression
Random Forest
Accuracy in Cross Validation 73.67% 81.19% 77.7%
Evaluation of Recommendation Error Using Normalized Distance-based Performance Measure (NDPM)
Rank by recommendation
Rank by user's actual ratings
Restaurant_1 Restaurant_1
Restaurant_2 Restaurant_3
Restaurant_3 Restaurant_7
Restaurant_4 Restaurant_2
Restaurant_5 Restaurant_4
Restaurant_6 Restaurant_9
Restaurant_7 Restaurant_8
Restaurant_8 Restaurant_5
Restaurant_9 Restaurant_10
Restaurant_10 Restaurant_6
Assessment of LDABOW (Bag of Words) LDA
Feature Dimension 10000 words in dictionary 15 topics
>99% dimension reduction
Computation Efficiency 2.5 hrs 15 min
>90% computation
time
(2000 samples)(10 fold cross validation)
Topic0 Topic1 Topic2 Topic3 Topic4
Japanese Mexican brunch Bar/ pizzaAtmosphere
sushi tacos breakfast great pizza
roll mexican coffee beer crust
pita salsa eggs happyhour wings
tuna burrito bacon bar thin
salmon chips pancakes drinks pepperoni
Topic5 Topic6 Topic7 Topic8 Topic9
Indian Asian fastfood sweets bbq
Indian Thai burger bagels cheese
buffet Pho fries cheese bbq
masala Chinese potato best sauce
naan soup Onion ring smoothies chicken
bianco curry dog Iove ribs
LDA (Latent DiriChlet Allocation) 15 topics
Topic 10: ComplimentGreat, best, live, good, like
Topic 11: Service ( Bad)Service, didn’t, never, even, back
Assessment of LDA
Dimension Reduction: 99% reduction in dimension
BOW (bag of words) features: 10,000 LDA features: 15 Topics
Computation efficiency: 2000 samples, 10 fold cross validation
BOW features: 2.5 hrs LDA features: 15 min
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