+ Recommending Branded Products from Social Media Jessica CHOW Yuet Tsz Yongzheng Zhang, Marco Pennacchiotti eBay Inc. eBay Inc.
+ E-Commerce vs. Social Media eBay 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 % 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 K th 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 (L knn, P knn ) (k=5)
+ K Nearest Neighbors Selection L knn : like-based KNN Recommend majority voting of liked brands from neighbors P knn: 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
+ 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
+ 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. b l: users who like brand b on Facebook b p : users who purchased at least one item in brand b on eBay |b l, b p |: 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