Price Sensitive Recommender System presented at the 3rd Data Science Summit Europe

1. Pricing & Discount Optimization
July 2016
Personal Price Aware
Recommender System: Evidence
from eBay
3rd Data Science Summit Europe
Asi Messica

2. Who We Are?
Asi Messica
A Phd student of Information Systems
and Software Engineering at the Ben
Gurion University.
Thesis: Price sensitive recommender
system
Prof. Lior Rokach
A data scientist and a full Professor of
Information Systems and Software
Engineering at the Ben Gurion
University.
An author of the Recommender System
Handbook.

3. Pricing & Discount Optimization
July 2016
Price Sensitive Recommender System
Personalization
Competition
Trends
Promotions
Dynamic Pricing

4. Hypothesis
• The maximum price a consumer is willing to
pay (WTP) for a product varies among
consumers
• It is possible to implicitly model consumers
willingness to pay from transactions history
• Incorporating consumers willingness to pay in
a recommender system will improve
recommendations effectiveness

5. Motivation
• Improve recommendations
• Promotion optimization

6. Experiment: eBay Market Place*
Different Prices by Different Sellers Simultaneously
• 6 Months of transactions and bids
historical data
• Free shipping, US only
• Sellers differ by their reputation
• “Buy now” and auctions
• Per transaction: date, consumer id,
product id, bid value/transaction
price, seller id, seller reputation
* eBay’s Big data lab academic cycle 2016. Thank you!

7. Price Aware Multi-Seller Recommender
System (PMSRS) Approach
1. Build a generic demand curve
2. Model personal demand curve taking
into account seller’s reputation
3. Personal price aware multi-seller
recommendation

8. Personal WTP* Modelling
Same Distribution for All Consumers, Different Parameters
* WTP – Maximum price a consumer is willing to pay
• WTP generic distribution curve per product
• Complementary cumulative curve is the demand curve.
• Personal Transaction price is the personal WTP distribution median.
• Context Aware Recommender System (CARS) method is used to predict
WTP of unseen products, taking into account seller’s reputation

9. Results: WTP Distribution Function
Logistic Distribution is Most Compatible but not Statistically Significant
WTP Distribution Function Willingness to Sell
Monthly bids generic distribution vs. logistic
distribution “Snow dvd”
Accumulative distribution function for sellers
with different reputation score

10. Results: WTP Prediction
Good Accuracy, Incorporating Seller’s Reputation Improves Prediction Accuracy
Average Bid Price = $28, Average Transaction Price = $47
Matrix Factorization (MF), Context Aware Matrix Factorization* (CAMF)
Seller’s reputation was modeled as contextual variable
WAPE: Weighted absolute percentage error
* Baltrunas et al. 2011

11. Results: Consumption Prediction
Incorporating Seller’s Reputation and Personal Demand Provides Best Results
Offering Ranking = Product Consumption (CARS) * Personal Demand
Implicit feedback. 10 Offerings with highest ranking vs. actual consumed transactions
Test: 2428 (explicit)
transactions, > 1M (implicit)
transactions,
488 consumers, 1,100 products
MPE Mean Percentage Error
(vs MF)

12. Conclusions
• WTP varies among consumers, it is possible to
implicitly model consumers WTP
• Incorporating personal WTP in a recommender
system improves recommendation accuracy
Inline with other research (Beladev et al Knowledge
based systems 2016, Greenstein-Messica et al Jasist
2017)
Promotion Optimization

13. Thank You!
asi.messica@gmail.com
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