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Recommender Systems and Product Semantics
Rayid Ghani & Andy FanoAccenture Technology Labs
Workshop on Recommendation & Personalization in E-CommerceMay 28, 2002
Who we are?Accenture Technology Labs
R&D Group for Accenture
~ 40 researchers in Chicago, Palo Alto (California) and Sophia Antipolis (France)
Research in Data Mining, Machine Learning, Ubiquitous Computing, Wearable Computing, Language Technologies, Virtual & Augmented Reality, Collaborative Workspaces…
What Does a Transaction Mean?
Terabytes of transaction data.
But what does any one transaction mean?
What does it tell us about the customer?
Example: Apparel
Transactional information captured by retailers: Date of Purchase SKU Price Size Brand
But what does this tell me about the customer who bought it?
Product Semantics:What does a product mean?
What does this shirt say about her?
Is it conservative or flashy?
Trendy or classic?
Formal or casual?
Where would we get this information?
Where do people get this information?Marketing
Product Companies and Retailers spend fortunes telling customers what their products mean.
Our idea:
Build a system that analyzes marketing texts to infer these attributes.
Example
From the Macy’s web site:
DKNY Jeans Ruched Side-Tie Tee
Get back to basics with a fresh new look this season. The Ruched Side-Tie Tee has a drawstring tie at left hip with shirred detail down the side. Stretch provides a flattering, shapely fit. V-neck.
Product Descriptions
Domain Experts
Product descriptionsmarked up with attribute values
SupervisedLearning Algorithm
Learned Statistical Models
Training the System
Inferring Attributes via Text Classification
Build one classifier per attribute type Simple statistical classifier – Naïve Bayes
Multinomial model (McCallum & Nigam 1998) For all words (description) and attribute values:
calculate P(word | attribute value) using the manually rated items
Given a new item description: Calculate P(attribute value | item description) for all
attribute values Use Maximum Likelihood
Semi-supervised Learning
Lot of product descriptions available for minimal cost
Labeling them is expensive Apply magical algorithms that combine labeled
and unlabeled data for classification EM (Nigam et al. 1999), Co-Training (Blum &
Mitchell 1999), Co-EM (Nigam & Ghani), ECo-Train (Ghani, 2002)
The EM Algorithm
Naïve Bayes
Learn from labeled data
Estimate labels
Probabilistically add to labeled data
Extremely Conservative
lauren
ralph
breasted
seasonless
trouser
jones
sport
classic
blazer
A Peek at the Learned ModelsNot Conservative
(Flashy)
rose
special
leopard
chemise
straps
flirty
spray
silk
platformBias Slip DressThe perfect black dress gets flirty and feminine in the bias-cut slip dress with sheer ruffled cap sleeves. A low, scoop neck and back is ultra-flattering while a draped, romantic fit reveals total elegance.
Lauren Single-Breasted BlazerSporty elegance and classic Gatsby-esque styling are captured in this impeccably designed single-breasted, three-button blazer from Lauren by Ralph Lauren. With traditional notch collar, signature button hardware, front flap pockets, and signature crest on left breast pocket.
Informal
jean
tommy
denim
sweater
neck
tee
hilfiger
formal
jacket
fully
button
skirt
lines
seam
crepe
leather
A Peek at the Learned Models
Polo Jeans Co. Muscle Logo TeeStrut your stuff in the Muscle Logo Tee. Flattering on the arms with a close-to-the-body fit, classic crewneck and shimmery logo print with stars. A sporty new basic for your tee collection.
BLACK TRIACETATE JACKET
A fresh alternative to classic suiting. Wear open for cardigan effect, buttoned for a clean look. Hidden placket with four tonal buttons and a hook-and-eye closure at the collar. Falls to hip. Lined.
Loungewear
chemise
silk
kimono
calvin
klein
august
lounge
hilfiger
robe
gown
Partywear
rock
dress
sateen
length:
skirt
shirtdress
open
platform
plaid
flower
A Peek at the Learned Models
ABS by Allen Schwartz Asymmetrical DressJust for the party girl with a big feminine streak. A ruffled one-shoulder cuts diagonally across the front and back. Accented with a rhinestone detail on the shoulder.
Extremely Sporty
sneaker
camp
base
rubber
sole
white
miraclesuit
athletic
nylon
Mesh
Juniors
jrs
dkny
jeans
tee
collegiate
logo
tommy
polo
short
sneaker
A Peek at the Learned Models
DKNY Jeans Jrs. Mesh Jersey SweaterAn innovative take on the football jersey, the see-through mesh sweater is a fashion favorite among the sporty set. Denim appliqué
Populating the Knowledge Base
NewProduct
Descriptions
Product descriptionsautomatically marked up with attribute values
Learned Statistical Models
Product Semantics Knowledge Base
Retailer’sWeb Site
ExtractedDescriptions of Products Browsed
Product Semantics Knowledge Base
Learned Statistical Models
EvolvingUser Profile
Query the Knowledge Base fo
r
Matching Products
Recommend Matching
Products to User
Recommender System
Advantages over Traditional Recommendation Systems
This approach provides us some of the underlying attributes that characterize a customer’s preference.
We can therefore begin to explain the preference rather than simply rely on the co-occurrence of purchases (e.g. people who bought x also bought y).
This helps with: Handling new products/rapidly changing products Low Frequency Products Cross Category Recommendations
Cross-Category Recommendations
Difficult for collaborative filtering and content-based systems
Build a model of the user - personality, stylistic attributes
Taste in clothing might also be suggestive of taste in other products, say furniture and home decoration
Create models for different product classes and create mappings among these models
Summary
“Understand” a product and hence the customer
Use Text Learning (supervised and semi-supervised) to abstract from product (description) to subjective, domain-specific features
Effective for new (and low frequency) products and for cross-category recommendations