AVLAB in Scientific Network Environment CNIC,CAS Yongzheng Ma myz@cnic
+ Recommending Branded Products from Social Media Jessica CHOW Yuet Tsz Yongzheng Zhang, Marco...
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Transcript of + Recommending Branded Products from Social Media Jessica CHOW Yuet Tsz Yongzheng Zhang, Marco...
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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