1. The Yelp Dataset Challenge
Predicting User Ratings

2. Overview The Yelp Dataset Creating the Data Matrix
Creating the Training Data
Grouping and Completing
Results and Problems
Further Ideas

3. The Yelp Dataset
Creating the Data Matrix
Creating the Training Data
Grouping and Completing
Results and Problems
Further Ideas

4. The Yelp Business Dataset
61,184 Businesses in 10 Cities
Extracted Data
Business IDs
City
Categories
Average Rating (compare to results)

5. MATLAB code to find Restaurants in Phoenix
string = 'Phoenix';
for i = 1:n %n is the total number of businesses
if findstr(string,city_business{i})
Phoenix = [Phoenix; i]; %has the indices for all Phoenix businesses
end
… %getting the business IDs store in PhoenixBusID (13601x1)
%Now we find which of those businesses are 'restaurants'
string2 = '"Restaurants"';
for i = 1:n
if findstr(string2,categories_business2{i})
Restaurants = [Restaurants; i]; %indices for all Restaurants
%Find the Restaurants in Phoenix
PhoenixRest = intersect(Phoenix,Restaurants);  2653
%Next, find all the business ID's the restaurants in Phoenix

6. The Yelp Review Dataset
1,562,264 Reviews
Idea
Filter out Restaurants in Phoenix using Business IDs
Extracted Data
User IDs
User Ratings

7. MATLAB: Ratings of Restaurants in Phoenix
for i=1:2653 …
for k=j: %optimized for last business in Phoenix
if findstr(stringm,business_id_review{k})
record1 = [record1;k];
label = [label;i];
else flag = 1;
break
end
 3 hours later… ratings from 45,867 users (3:1 ratio  BAD)

8. Idea: every person is a linear combination of a group similar people
Given user ratings 𝑏= and a group of similar users 𝐴= ,
Delete all 0 rows in 𝑏: 𝑏 𝑛𝑒𝑤 = (here, row 3 and 5). Delete the same rows in 𝐴: 𝐴 𝑛𝑒𝑤 =
Solve 𝐴 𝑛𝑒𝑤 𝛼= 𝑏 𝑛𝑒𝑤 using LASSOpos and get 𝛼=
Go back to the original matrix 𝐴: A𝛼= 𝑏 𝑐𝑜𝑚𝑝 where 𝑏 𝑐𝑜𝑚𝑝 = round to get 𝑏 𝑐𝑜𝑚𝑝 =
Finally, if necessary replace falsely created entries in 𝑏 𝑐𝑜𝑚𝑝 .
Say why we choose LASSO: because it gives us a linear combination of A to get b, then we can use this as a means for Matrix completion

9. The Yelp Dataset
Creating the Data Matrix
Creating the Training Data
Grouping and Completing
Results and Problems
Further Ideas

10. Creating the Data Matrix
Challenge: never lose track of indices
Need to know the business to be able to validate
3:1 ratio needs to get better
Step 1: Find all users >20 entries (596)
Corresponding to 2234 businesses
< 1% filled  BAD
Mention that some users had multiple reviews per one restaurant – decision: overwrite those since last one is latest one

11. Improving the Data Matrix
Trim: Restaurant > 20, Users > 15
198 Restaurants, 274 Users
86% zeros (sort of bad)
BAD results
198∗274
Trim: Restaurant > 45, Users > 26
48 Restaurants, 111 Users
only 66% zeros
MUCH better results
48∗111
users
users
business
business

12. A small part of the Bad Matrix

13. A small part of the Good Matrix

14. The Yelp Dataset
Creating the Data Matrix
Creating the Training Data
Grouping and Completing
Results and Problems
Further Ideas

15. Creating the Training Sets
x1 = find(BestMatrix(:,k)>0); %find the indices of ratings in column k %and find what those ratings are x1sratings = BestMatrix(x1,k); % create matrix with only places the user k rated … x1_others_trim_index = find(x1_countotherreviews >5); %5 or more same ratings %find which users are most equal if abs(x1sratings(j)-x1_others_trim(j,i))==0 %find where same ratings %count on how many reviews they agree  then find most un-like users  unique to each user

16. The Yelp Dataset
Creating the Data Matrix
Creating the Training Data
Grouping and Completing
Results and Problems
Further Ideas

17. Grouping Semi- and Unsupervised Spectral Clustering
Create Kernel Matrix
Use Training points (unique to each user)
each user gets individual Binary Classification
% Kernel Matrix %
for i = 1:n
for j = 1:n
v = -(norm(BestMatrix(:,i)- BestMatrix(:,j),2)^2);
W(i,j) = exp(v/(2*sigma^2));  we have a choice of sigma
end
% Construct L
L = diag(sum(W,2),0) - W;

18. Supervised Unsupervised for i = 1:column % Compute second eigenvector
ingroup = …;  the training data YES inopposite = …;  the training data NO
b = zeros(n,1);
b(indgroup) = 1;
b(inopposite) = -1;
M = diag( abs(b), 0);
lambda = 1000;
g = (L + lambda*M) \ (lambda*M*b);
Cest1 = double(g>0)+1;
userfriends{i} = find(Cest1==2);
end
% Compute second eigenvector
[V,E] = eigs(L,2,'sm');
f = V(:,1);
Cest = double(f>0)+1;
Group1 = find(Cest == 1);
Group2 = find(Cest == 2);

19. LASSOpos.m One change to the last sub-function of LASSO.m:
function m = PROX(z, lambda, dt)
c = lambda*dt;
m = (z-c).*double(z -c > 0) %+ (z+c).*double(z + c < 0);  delete
 Only positive entries in 𝜶
Perform Lasso on every user
Assemble into New Matrix
The matrix is completed

20. Summing up the Approach
Training Points  Semi-Supervised  Lasso
Unsupervised  Lasso
Lasso (no grouping)
Remember from the homework that unsupervised spectral clustering gave really bad results

21. The Yelp Dataset
Creating the Data Matrix
Creating the Training Data
Grouping and Completing
Results and Problems
Further Ideas

22. Verifying Results Note: there is no original matrix to compare to
Pick random user, delete given entries complete the user, compare to given entries
Taking average rating of completed compare to
Current Average Rating of given Yelp Data for Business
Average Rating of used data (before completion)
Average Rating of User
Unlike in class, we do not have the actual matrix that we complete;

23. Errors for BAD Matrix Deleting given entries of a Random User
Calculate difference: actual vs created entries (25 ratings)
Semi-Supervised Learning
Average of 1.44 points off from each given rating
vs. RANDOM predictions are 1.52 points off
LASSO only
slightly better results
Recall BAD matrix has 86% zeros i.e. 16% entries; mention that it should be given that we always use LASSO
Given 25 reviews, the difference was off by 36, random is 38; mention that this means that still a bunch are correct but many are VERY far off

24. Errors for GOOD Matrix Deleting given entries of a Random User
Calculate difference: actual vs created entries (28 ratings)
Semi-Supervised Learning
Average of points off from each given rating
Control: RANDOM predictions are 1.5 points off
Unsupervised Learning
Average of points off from each given review
LASSO only
0.92 points off from each given review
Recall: Good matrix has 66% zeros, i.e. 34% entries

25. Values from example User (Semi-Supervised)5 3 4 2 5 3 4 2
Left Column: True Value
Right Column: Predicted Rating
Exact
Off by 1
Off by > 1

26. Errors for GOOD Matrix Comparing Averages of User Ratings
Semi-Supervised
Average of original vs predicted Ratings of all Users
Before:
After:
Difference:
We noted: the averages are always a little bit less; we overwrite 7’s etc

27. Errors for GOOD Matrix Comparing Averages of Business Ratings
Semi-Supervised
Average of original vs predicted Average of Business
Example: Pane Bianco (Pizza Place in Phoenix)
Yelp: 4.0
Before (using our users): 4.12
After: 3.64
Difference: 0.36 and 0.48
Current on yelp means including all users that we threw out

28. The Yelp Dataset
Acquiring the Data Matrix
Creating the Training Data
Grouping and Completing
Results and Problems
Further Ideas

29. Further Ideas
Take predictions from smaller matrix, use to make bigger matrix make less sparse, complete again
Apply to different cities and compare results
Use just good ratings, overwrite bad ratings with 0
Go back to certain points where subjective decision were made, use different approach i.e. how to make training set
Look at quality of review (in original yelp data)

30. The End
Presentation by
Daniel Hallman &
Maike Scherer