•Personalized Review Engine for Yelp Users

•Yelper Helper

•Alex Ruiz-Euler•08/2014

•MVP•MVP

Ye •Yelper Helper

•PROBLEM •SOLUTION

•MVP•MVP•Yelper Helper: Overview.

•Determine usefulness of new reviews

•Compute user similarity

•User making query

•MVP•MVP•Yelper Helper: Overview.

•Determine usefulness of new reviews

•Compute user similarity

•User making query

•MVP•MVP•Yelp Reviews

•Useful tags

•Review

•User

•Review attribute

s

•User attributes

•Business attribute

s

•Useful

tags

•1 •Abe

• Vocabulary richness, stars...

• no. reviews, average rating...

•Average rating...

•3

•MVP•MVP •Predicting Number of “Useful” Tags

•Data structure (Las Vegas):

•363,691 reviews

•112,702 users

•3,536 businesses

• (source: Yelp Academic Dataset)

•MVP•MVP •Validation: Poisson regression / Comparing AIC.

• Feature Selection

•Model Selection

•MVP•MVP•Yelper Helper: Overview.

• Predict •usefulness

of new reviews

•Compute user similarity

•MVP•MVP•Yelper Helper: Overview.

• Predict •usefulness

of new reviews

•Compute user similarity

•MVP•MVP •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 •1•Rest

2•1 •1

•Rest 3

•1 •1 •1•Rest

4•1 •1 •1

•V: Restaurant profile

•2

•MVP•MVP •User profile matrix

• Hipster

• Divey

• Upscale

• Intimate

• Touristy

• Classy

• Romantic

• User 1

•3 •1 •33 •1• User

2•2

• User 3

•1 •1 •1• User

4•3

•1•3 •2 •1 •3 •1•5 •4 •4 •5

•2 •3•1 •2 •3

•1•3

•MVP•MVP •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

•MVP•MVP •About Me – Alex Ruiz-Euler (PhD Political Science, 2014)

•MVP•MVP

•Thank You.

•MVP•MVP

•MVP•MVP

•MVP•MVP

•MVP•MVP•Problem: ~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.