Download - Yelper Helper Concept
Personalized Review Engine for Yelp Users
Yelper Helper
Alex Ruiz-Euler08/2014
Ye Yelper Helper
PROBLEM SOLUTION
MVPMVPYelper Helper: Overview.
Determine usefulness of new reviews
Compute user similarity
User making query
MVPMVPYelper Helper: Overview.
Determine usefulness of new reviews
Compute user similarity
User making query
MVPMVPYelp Reviews
Useful tags
Review UserReview
attributesUser
attributesBusiness attributes
Useful tags
1 AbeVocabulary richness, stars...
no. reviews, average rating...
Average rating... 3
MVPMVP Predicting Number of “Useful” Tags
Data structure (Las Vegas):
363,691 reviews
112,702 users
3,536 businesses(source: Yelp Academic Dataset)
MVPMVP Validation: Poisson regression / Comparing AIC.
Feature Selection
Model Selection
MVPMVPYelper Helper: Overview.
Predict usefulness of new reviews
Compute user similarity
MVPMVPYelper Helper: Overview.
Predict usefulness of new reviews
Compute user similarity
MVPMVP Use-taste matrix / Restaurant-category matrix
U: Ratings (stars)
Rest 1 Rest 2 Rest 3 Rest 4
User 1 1 3 2
User 2 2 4 1
User 3 2 1
User 4 1 2 1
Hipster Divey Upscale Intimate Touristy Classy Romantic
Rest 1 1 1Rest 2 1 1Rest 3 1 1 1Rest 4 1 1 1
V: Restaurant profile
2
MVPMVP User profile matrix
Hipster Divey Upscale Intimate Touristy Classy Romantic
User 1 3 1 33 1User 2 2User 3 1 1 1User 4 3
13 2 1 3 15 4 4 5
2 31 2 3
13
MVPMVP Similarity Matrix – Euclidean Distance Over UV.
User 1 User 2 User 3 User 4
User 1 0
User 2 1.5 0
User 3 2 3.4 0
User 4 7.2 1 2 0
MVPMVP About Me – Alex Ruiz-Euler (PhD Political Science, 2014)
MVPMVP
Thank You.
MVPMVP
MVPMVP
MVPMVP
MVPMVPProblem: ~75% of Yelp reviews have 0 “useful” tags*.
(* Las Vegas sample.)
Issues with data
For similarity:
Attributes of users in Yelp are about activity, not preferences.
→ Uncover taste preferences with collaborative filtering.
For prediction:
Prediction of usefulness of review:
a) Too many zeros (zero-inflated!). Weird results (null vs. full).
→ Zero-inflated Poisson model.