+

Recommending Branded Products from Social Media Jessica CHOW Yuet Tsz

Yongzheng Zhang, Marco Pennacchiotti

eBay Inc. eBay Inc.

+E-Commerce vs. Social MediaeBay users connected to their Facebook accounts

Access to basic social information, the set of liked pages

Goal:

Boost user interaction and adoption

Improve recommendation and better predict purchase behaviors

Cold start problems can be solved

+What we do

Study the correlation between brands liked and purchased

Brand prediction system

+Two Main Techniques

Collaborative filtering

User-user systems, Item-item systems

Content-based

Assumption:

Users shared on the social network reflects their own interests

+Basic Information of Dataset

Restricted set of Facebook information:

Demographic Information (Gender, Age)

Liked pages on Facebook

9,398 eBay Facebook users (from 13,619 users)

4445 Brands from eBay database

+

eBay : 4445 Brands

Stored information for each user:

1. List of Facebook likes on Meta-Categories

2. List of purchased items by brands

3. Demographic Information (Age, Gender)

+

Distributions

No. of Liked Brands

Max.: 373

Average: 12

Median: 6

No. of Likes

Max.: 2814

Average: 38

Median: 5

No. of Purchased Brands

Max.: 222

Average: 8

Median: 5

+Different Demographic Groups

57% aged 25-44

65% of Women

Distinctive Purchase Behaviors

Women buy significantly more (795 brands, fashion and home )

Men buy significantly more (104 brands, sports and electronics)

Gender and Age are important signals for recommending

+Purchase Probability

P(u ,b) : Probability of a user u, buying in brand b

purc(u, b): number of purchases of u from brand b

purc(u, B): number of purchases of u from all brands

P(u)k: probability of user u buys from the Kth favorite brands

K: ranking from the purchase probability

U: No. of all users

Overall Purchase Focus

+Users do not buy brands randomly> 45% of the times

buying from the first preferred brand

18% of the times

buying from the second preferred brand

Top 5 brands:

Collectively for about 85% of a user’s purchased brand

Users express strong personal interests for certain brands

+Brand Selection Module

(10-fold cross validation, 90% training, 10% testing)

Baseline selection (B1, B2) B1: Recommend the most popular 8 brands to all users

B2: Recommend what user has liked on Facebook

KNN Selection (Lknn, Pknn ) (k=5)

+K Nearest Neighbors Selection

Lknn : like-based KNN

Recommend majority voting of liked brands from neighbors

Pknn: Purchase-based KNN

Cos (u, v): cosine similarity between target user u and a user v (Gives higher weight to the closest neighbors)

purch (v, b): the number of items bought by user v from brand b

purch (v, B): the number of items bought by user v from all brands

+Any Improvement?

Related Brands!

Calculate Relatedness between any pair of brands b1 and b2

: users who have purchased both brands

: users who have purchased either brand or both

Reject relatedness score that is below 0.30

+Evaluation Measures

Confusion Matrix of Purchased and Recommended Brands

Precision:

P = =

Recall:

R = =

+Summary

(10-fold cross validation, 90% training, 10% testing)

+Logistic Regression

Pre-filtering

Avoid noisy data

Change in threshold, change in users coverage

Percentage of users coverage

+Evaluation Results of Algorithms

evalusation

+Evaluation Results of Algorithms

evalusation

Precision =

Recall =

+Brand Recommendation System Predict or recommend brands for purchases

Using Facebook likes and basic information only

Steps:

1. Pre-filtering (Logistic Regression: Thresholds of 0.5 & 0.8 )

2. Brands Selection (KNN Selection, Baseline selection)

3. Expansion of Related Brands

+

Recommending Branded Products

from Social Media

The End

Jessica CHOW Yuet Tsz

+

Appendix

+Correlation between likes and purchases

PMI: Pointwise mutual information

Measures the degree of association between two events.

bl: users who like brand b on Facebook

bp: users who purchased at least one item in brand b on eBay

|bl, bp|: Number of users who liked and purchased brand b

U: total number of users

+

The higher the PMI Score, the stronger the correlation

+Full Results of LR + Pknn + R