1. Food Recommendation using Personal Popularity Tendency
By Jenting (JT) HSU

2. Background Information
Food is
Discrimination problems
Long queues

4. Recommendation Systems
Collaborative Filtering
Content Based Filtering
Hybrid Systems

5. Novel Recommendation based on personal popularity tendency
Recommendation systems tend to recommend popular things.
Case 1: Ineffective
Case 2: Non-diversified
Tangent: Significantly Lower Accuracy

6. Data Set MovieLens Users with 20+ ratings
Only consider positive ratings
80% Training Set
20% Testing Set
Assumption: Popularity is reflected by total earnings

8. Personal Popularity tendency
User A
User B

9. Personal popularity tendency matching
𝑀𝑎𝑥 𝑖 𝑝 𝑖 𝑧 𝑖 −𝑐∗𝐷EMD(R,P)
p: preference score by ItemRank
z: 1 or 0
c: weight parameter
𝐷EMD(R,P): earth mover distance

10. Why earth mover distance
Kullback-Leighbler Divergence
Example: 1 , 2 , 10
|1-2|=1
|2-10|=1 ???

11. Personal popularity tendency matching
Double Optimization
Complexity of ( 𝑛 𝑘 ) 𝑘
n : number of items
k : number of items in recommendation set
𝑀𝑎𝑥 𝑖 𝑝 𝑖 𝑧 𝑖 −𝑐∗𝐷EMD(R,P)

12. Accuracy

13. Integration Recommendation Systems  iOS App
“You are recommended to order the following dish: Dish A and Dish B”

14. Cheeseburger, 30HKD

15. Updating itemrank score
𝑀𝑎𝑥 𝑖 𝑝 𝑖 𝑧 𝑖 −𝑐∗𝐷EMD(R,P)
[item 1, item 2, item 3, item 4, ….. , item 20]
[ , , , , , ] (numbers can only range from 0~1)
Plus or Minus: 1∗α (α is the learning rate ranging from 0~1)

16. Q&A