Presentation of the Recommendation Subsystem that was built for the Museum Radar application during the 2nd ELLAK Summer Code Camp at Harokopeio University. This is the same presentation that was used during the Presentations Day on 29/4/2015.

1. Recommendation
Subsystem
Museum Radar
Panagiotis K. Gemos

2. The best way to learn is by asking
questions.
- I said that

3. What’s the subsystems purpose ?

4. What’s the subsystems purpose ?
It uses an algorithm to rearrange the museum list
that is given as input in a way that it best fits
what the user wants.

5. What’s the subsystems purpose ?
It uses an algorithm to rearrange the museum list
that is given as input in a way that it best fits
what the user wants.
Its purpose is to make suggestions based on info
about the museum.

6. What’s the subsystems purpose ?
It uses an algorithm to rearrange the museum list
that is given as input in a way that it best fits
what the user wants.
Its purpose is to make suggestions based on info
about the museum.
Hint: We do not use mind reading machine. That
might be our next project. Thus no 100% accuracy

7. So, what did we use ?

8. So, what did we use ?
We used an algorithm that produces suggestions
based on :

9. So, what did we use ?
We used an algorithm that produces suggestions
based on :
the distance of the museum
from the user

10. So, what did we use ?
We used an algorithm that produces suggestions
based on :
the distance of the museum
from the user
and the museum’s ratings.

11. The algorithm’s skeptic

12. The algorithm’s skeptic
It’s just an equation that does it’s job.

13. The algorithm’s skeptic
It’s just an equation that does it’s job.
It adjusts weights to the parameters:

14. The algorithm’s skeptic
It’s just an equation that does it’s job.
It adjusts weights to the parameters:
o Distance – 40%

15. The algorithm’s skeptic
It’s just an equation that does it’s job.
It adjusts weights to the parameters:
o Distance – 40%
o Rating – 60%

16. The algorithm’s skeptic
It’s just an equation that does it’s job.
It adjusts weights to the parameters:
o Distance – 40%
o Rating – 60%
…and according to these, it sorts the input list of
museums.

17. The algorithm’s skeptic
It’s just an equation that does it’s job.
It adjusts weights to the parameters:
o Distance – 40%
o Rating – 60%
…and according to these, it sorts the input list of
museums.
The sorted list is the output.

18. Why did we chose a 60-40 ratio ?

19. Why did we chose a 60-40 ratio ?
 We are convinced that users are more attracted to
others users’ ratings in general. So the ratings parameter
should have a greater influence to the suggestion metric
than the distance parameter.

20. Why did we chose a 60-40 ratio ?
 We are convinced that users are more attracted to
others users’ ratings in general. So the ratings parameter
should have a greater influence to the suggestion metric
than the distance parameter.
 The 60-40 is a ratio that we believe it fits best.

21. Why did we chose a 60-40 ratio ?
 We are convinced that users are more attracted to
others users’ ratings in general. So the ratings parameter
should have a greater influence to the suggestion metric
than the distance parameter.
 The 60-40 is a ratio that we believe it fits best.
Hint: No we haven’t done any statistical
analysis to find that 60-40 ratio.

22. Behold the maths :

23. Behold the maths :

24. Nice maths pal, where is the coding ?

25. Nice maths pal, where is the coding ?
 The algorithm is written in the famous PHP language and
its code has been inserted to the Museum REST controller
(Mymuseum.php).

26. Nice maths pal, where is the coding ?
 The algorithm is written in the famous PHP language and
its code has been inserted to the Museum REST controller
(Mymuseum.php).
 It is called by the function calc_weight which receives:

27. Nice maths pal, where is the coding ?
 The algorithm is written in the famous PHP language and
its code has been inserted to the Museum REST controller
(Mymuseum.php).
 It is called by the function calc_weight which receives:
o the museum’s rating

28. Nice maths pal, where is the coding ?
 The algorithm is written in the famous PHP language and
its code has been inserted to the Museum REST controller
(Mymuseum.php).
 It is called by the function calc_weight which receives:
o the museum’s rating
o the museum’s distance

29. Nice maths pal, where is the coding ?
 The algorithm is written in the famous PHP language and
its code has been inserted to the Museum REST controller
(Mymuseum.php).
 It is called by the function calc_weight which receives:
o the museum’s rating
o the museum’s distance
o the max distance and max rating

30. Looking into the Future…

31. Looking into the Future…

32. Looking into the Future…
 We may add extra parameters for the recommendation
algorithm like:

33. Looking into the Future…
 We may add extra parameters for the recommendation
algorithm like:
o The time till the museum closes in accordance with the average
time that a person spends on a museum.

34. Looking into the Future…
 We may add extra parameters for the recommendation
algorithm like:
o The time till the museum closes in accordance with the average
time that a person spends on a museum.
o The search history of the user, recommending museums that he
have searched in the past.

35. Looking into the Future…
 We may add extra parameters for the recommendation
algorithm like:
o The time till the museum closes in accordance with the average
time that a person spends on a museum.
o The search history of the user, recommending museums that he
have searched in the past.
o The ability to return a list that uses a selection of the above
parameters which will be defined by the user.

36. What we got from the Code Camp

37. What we got from the Code Camp
 Lots of experience.

38. What we got from the Code Camp
 Lots of experience.
 Learned more about web programming.

39. What we got from the Code Camp
 Lots of experience.
 Learned more about web programming.
 We did not know PHP and we had to learn it in half week.

40. What we got from the Code Camp
 Lots of experience.
 Learned more about web programming.
 We did not know PHP and we had to learn it in half week.
 We got to cooperate with other programmers.

41. What we got from the Code Camp
 Lots of experience.
 Learned more about web programming.
 We did not know PHP and we had to learn it in half week.
 We got to cooperate with other programmers.
 We got close into how a larger scale program than the tic-tac-
toe can be developed.

42. Is it Thanksgiving day ?

43. Is it Thanksgiving day ?
 Although it might not be, I would like to thank all the
people behind the Harokopeio Ellak Code Camp and
the group I worked to make the project.

44. Is it Thanksgiving day ?
 Although it might not be, I would like to thank all the
people behind the Harokopeio Ellak Code Camp and
the group I worked to make the project.
 Special thanks to Christos Tsolkas to whom we owe this
algorithm, because basically it was his idea. The one I
wrote was too complex and had a 50-50 weight ratio.
He also helped as to insert the algorithm inside the
controller (actually he inserted it).

45. My questions are off

46. My questions are off
but…

47. Maybe you got some questions for me