1. This document proposes solutions to the "cold start" problems of new users and new locations in travel recommendation systems. 2. It suggests using personal user attributes like age and gender to find similar existing users to recommend locations for new users, and using location tags from Google Places to find similar existing locations to recommend to users. 3. An experiment applying these approaches achieved meaningful accuracy according to the results presented.

1. Application of User Attributes &
Location Tag Similarities
to Handle the Cold Start Problem in
Travel Recommendation Systems
By
Senuri Wijenayake & Supunmali Ahagnama

2. Journey Today..
- Introduction to Travel Recommendation Systems
- The Cold Start Problems in Recommendations
- Existing Approaches & Limitations
- Proposed Solution for the New User Problem
- Proposed Solution for the New Location Problem
- Methodology
- Experimental Results
- Discussion

3. Why Personalized Travel Recommendation?
Deciding where to visit in your leisure time
could be a tedious and an exhausting
activity to many users due to the following
reasons.
➔ Information Overload
“Too many locations and Too many
websites! How do I know where I want
to go next?”
➔ Time Consuming
“I don’t have time or the effort to plan
the trip.”
➔ Expensive!
“Time is money!“

4. The requirement for Personalized Travel
Recommendation Systems are apparent and are
available!
Tip
User based collaborative
filtering approach makes
recommendations based on
the idea that a user will
rate an unvisited location,
similar to how top similar
users have rated that
location.
BUT,
The existing solutions
implementing user-based
collaborative filtering do not
provide robust solutions to the cold
start problem!

5. How is the Traditional Similarity Measured?
Traditionally, in user based collaborative filtering mechanism the similarity between any two
users are measured based on,
1. Locations they have both visited
2. How they have rated the visited locations
3
Up
4
4
5
4
4
5
3
Uq
Assume that the similarity between the users P and
Q needs to be calculated.
Step 1 : Identify the common places they have both
visited
Step 2 : Apply their ratings to the common locations
to the Pearson’s Correlation Coefficient to
determine positive/ negative correlation

6. How does the Reccomendation Process
Ususally Work?
Looking at the normal process (Adomavicius and Tuzhilin, 2005) , when we need to predict the
rating of the active user P for location L,
Step 1: Find the top similar users (assume the set is represented by U) who have been to
the location L
Step 2: Find the rating for the user P, by weighting the rating given by each user in U to
the location, by the similarity between the relevant user and P
P

7. The New User Problem
Tip
A New User is any user
without any travel
experience.
Thus, there is no way for
us to compare this user
to other users in the
system, to make
recommendations!
A user without any experience can’t be given recommendations by the normal approach.
Literature suggests,
- Recommending popular landmarks in the given region
- Recommending random landmarks in the region
BUT these solutions lack Personalization!
RQ1: Can personal attributes of users such as age and gender
be used to improve the personalized recommendations for new
users?
Use the age range and the gender of the active user to find the similar users
who have been to the location that needs to be rated.
Our argument is that, the active user will rate the location similar to the way
the similar users rated!

8. Rationale Supported by Survey

9. The New User Problem – Proposed Solution
(1)
(2)
Q
L
Up

10. The New Location Problem
Tip
A New Location is any
location that has not
been visited by any
other user in the system.
Thus, we need a new
method to compare
locations with one
another!
A location without any visits can’t be recommended to any user as we don’t know anything
about this location!
Literature suggests,
- Making suggestions only using the locations the other users have visited
BUT these solutions are not robust to identify the unique features of different locations!
RQ2: Can location tags extracted from Google Places be used
to improve personalized recommendations for new locations?
Each location coming from Google has a “types” tag which describes the
location => restaurant, lodging, temple etc.
Our argument is that, the active user will rate the new location the same way
he rated similar locations in the region.

11. Google Places
Google Places is a popular database with
thousands of location profiles.
Each location profile that can be extracted from
Google Places, will come with a “types” array,
which will consists of an array of tags describing
the features of the location.
These features can be used to find similarities
between locations, that can not otherwise be
found.

12. The New Location Problem – Proposed Solution
(3)
(4)
Q
L

13. Methodology
1. The public profile and the Facebook check-ins of 45 undergraduate
students from two universities along with their explicit rating for each
location visited were extracted to create the data set.
2. Location profiles were extracted from Google Places based on the
location of the user.
3. The training set was created using 50% of check-ins of 40 users and the
rest was used for testing purposes.
4. A user-based collaborative filtering mechanism which supports the
assumptions was implemented in Python.

14. Experimental Results
Mean Absolute Error : 0.9437
Root Mean Square Error : 1.2557
Scenario Number of Hits
Existing User – Existing Location 359
Existing User – New Location 206
New User – Existing Location 89
New User – New Location 35
The public profile and the Facebook check-
ins of 45 undergraduate students from two
universities along with their explicit rating for
each location visited were extracted to create
the data set.
The training set was created using 50% of
check-ins of 40 users and the rest was used
for testing purposes.
It was observed that the new location
scenario is more frequently handled by the
system than the new user scenario.

15. Conclusion
Based on the above results it can be
concluded that with regard to travel
recommendation systems, personal
attributes such as age and gender can be
used to handle the cold start problem for a
new user and factors such as region and tag
similarity can be used to solve the new
location problem with considerable
accuracy and efficiency.

16. Summary
1. Personalized Travel Recommendation Systems are much needed.
2. User-based Collaborative filtering is a popular technique used in travel
recommendations.
3. Cold Start is an inherent issue in Collaborative Filtering, which requires
robust solutions.
4. This presentation proposed two robust approaches to handle the New
User and New Location Problem using the personal attributes between
users and location tags extracted from Google Places respectively.
5. The experimental results establish that the approach proposed can make
recommendations with a significant accuracy.