Insight data science presentation.

1. Meet You Where?
Josh Friedman

2. Meet You Where?
Josh Friedman

3. Meet You Where?
Josh Friedman

4. Meet You Where?
Josh Friedman

5. Meet You Where?

6. Meet You Where?
?

7. Meet You There
1.Identify an equitable meeting location.
I Travel 10 minutes
They Travel 10 minutes

8. Meet You There
1.Identify an equitable meeting location.
I Travel 10 minutes
They Travel 10 minutes

9. Meet You There
1.Identify an equitable meeting location.
I Travel 10 minutes
They Travel 10 minutes

10. Meet You There
1.Identify an equitable meeting location.
2. Finds restaurants near the ideal meeting place.

11. Meet You There
1.Identify an equitable meeting location.
2. Finds restaurants near the ideal meeting place.
3.Identifies the best ones for you.
• Travel fairness ratio.
• Best reviewed.
• Lowest Price.

12. Behind the scenes
Traffic Data
Calculate
Midpoint

13. Behind the scenes
Search Location
Traffic Data
Calculate
Midpoint

14. Behind the scenes
Ranked Locations
1. Ray's Pizza
2. World-Famous Original Ray's Pizza
3. Ray's Original Pizza
4. Famous Ray's Pizza
Search Location
Cosine Similarity
Traffic Data
Calculate
Midpoint

15. Behind the scenes
Ranked Locations
1. Ray's Pizza
2. World-Famous Original Ray's Pizza
3. Ray's Original Pizza
4. Famous Ray's Pizza
User Selection:
Search Location
User,
Review,
Price,
Travel Fairness Ratio
Cosine Similarity
Traffic Data
Calculate
Midpoint

16. Behind the scenes
Ranked Locations
1. Ray's Pizza
2. World-Famous Original Ray's Pizza
3. Ray's Original Pizza
4. Famous Ray's Pizza
User Selection:
Search Location
User,
Review,
Price,
Travel Fairness Ratio
Cosine Similarity
Traffic Data
XCalculate
Midpoint

17. The Cold Start Problem – Where do people tend
to go?
More Expensive
Average Price (x) and Review (y)
selections for 30K people.
Higher Rating

18. Spur of Moment
The Cold Start Problem – Where do people tend
to go?
Gourmets
More Expensive
Higher Rating
+
+
+

19. MeetMeWhere.xyz

20. The Cold Start Problem – Where do people tend
to go?

21. Rank Locations by Cos Similarity to User Profile
𝜈local = (R・vi ) + (P・vj)+ (T・vk)
𝜈user = ( 𝛚R・vi ) + ( 𝛚 P・vj)+ ( 𝛚 T・vk)
Place Review (1-5) Travel Fairness (0-100%)Price Range (0-4)
Rank = 𝜈local ・ 𝜈user

22. Meet You Where?
I travel 10 min
They travel 10 min
1. Calculate the mid-point based upon projected real time
traffic data or public transit delays (google)

23. Meet You Where?
I travel 10 min
They travel 10 min
1. Calculate the mid-point based upon projected real time
traffic data or public transit delays (google)

24. Meet You Where?
I travel 10 min
They travel 10 min
1. Calculate the mid-point based upon projected real time
traffic data or public transit delays (google)

25. Meet You Where?
I travel 10 min
They travel 10 min
1. Calculate the mid-point based upon projected real time
traffic data or public transit delays (google)

26. Meet You Where?
1. Search the vicinity of the mid-point for establishments
matching the user query (google places API)

27. Meet You Where?
The Best One! … But what's important? Review? Price?
Distance? Some combination?

28. Behind the scenes
Ranked Locations
1. Ray's Pizza
2. World-Famous Original Ray's Pizza
3. Ray's Original Pizza
4. Famous Ray's Pizza
User Selection
Search Location
User, Review, Price, Travel Fairness Ratio
Cosine Similarity
User Preferences
Traffic Data
Calculate
Midpoint