MSDS Capstone Project Poster - project title "Text Enhanced Recommendation System Model Based on Yelp Reviews"

1. DataScience@SMU
Language Empowered Recommendation
Albert Asuncion, Peter Kouvaris, Ekaterina Pirogova, Hari Sanadhya, Arun Rajagopal
Master of Science in Data Science
Southern Methodist University, Dallas, TX 75275, USA
Aligning the Stars
Our star factor leverages two convolutional neural networks
that are tasked with learning how review text affects ratings
for users and businesses separately.
These results are combined to produce a new star rating
based on a mixture of the user, item, and review text.
An example of this would be if User A frequently wrote
negatively worded reviews, but upon going to Business B
wrote a highly positive one, our first neural network would
weight this derivation very highly.
Our other network would consider the same logic for
Business B as if it was a user giving reviews above. If all
user’s generally give Business B positive reviews, this should
be considered and produce a slightly lower star rating. The
average of these is used.
Ethics
Measuring Success
The success of a star rating system can be measured by the
normalization of data and the improvement in
recommendations on a subset of sample data. Our method
performs 22% better using the FCP statistic.
The importance of a recommender's systems results on
users and businesses can be drastic, requiring there to be
clear laws and privacy rights associated with these. Think of
these examples:
• An influential Yelp user is threatened by a business.
• A small business is banned from Yelp.
• Lack of privacy for users
Recommender systems are ubiquitous, driving us to web and
mobile applications for help deciding which movies to see,
books of interest to read, or restaurants to enjoy our next great
meal. We examined one of the most widely known and
recognized of these recommender systems and improved
upon its star rating and recommender system. More
specifically, we examined the following questions:
• How do we make ordinal star ratings more meaningful?
• How can we improve Yelp recommendations?
Introduction
Conclusions
• The use of Natural Language Processing makes star
ratings more meaningful.
• The text of review greatly improves the recommendation
system quality.
• Fake reviews penalty is too harsh in Yelp system.
1st Gen: Item, User, Rating
2nd Gen: Items, Users, Rating, Context, Time, Location,
Rating
3rd Gen: Ontologies (Complex informational web structures)
Evolution of Recommenders
The Yelp star rating is ambiguous and fraught with issues:
1. Yelp data is inherently handicapped
a) User time of visit is unknown
b) Most on non-mobile app users are not registered
c) Recommendation success/failure is difficult to measure
2. Ratings are biased (see Figure 1)
3. Distances between rating values are not equidistant
Failing Star
Collaborative filtering algorithms rely on information about
other users who have interacted with the item of interest.
These algorithms benefit greatly from numeric ratings that
scale in a linear way. This feature detailed in step 2 of Figure
3, allows us to :
• Provide better recommendations at lower training sizes.
• Leverage star ratings for filtering on other dimensions.
Star ratings are simple to understand and applied to a variety
of use cases. Building useful data metrics like these can
benefit more than just recommender models.
Importance of Star Shape
+ =
Basic Stars Sentiment Stars Deep Learning Stars
Figure 1: Using deep learning and language features we eliminate bias and
create equidistance between ratings.
Figure 2: Star distribution shift, improving from left to right.
Figure 3: Recommender system pipeline
Basic NLP
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