The slideshow was presented at ICMA Conference in Helsinki at the "How to Turn Big Data into Dollars" Workshop organized by Gravity R&D, The presentation reviews the heterogeneity of data sources at classified media, shows the massive size of data available, and give some insights how to use those data for personalization in various scenarios.

1. GRAVITY R&D
BIG DATA IN ONLINE CLASSIFIEDS
Domonkos Tikk, CEO/CSO
23 May, 2014

2. What data is available in your application domain?
Page views
User data
Ad placements
Popular products
Number of visits
Device
IP address
Time of browsing
Time spent on site
User behavior
Ad replies
Featured ads
Number of products
Location
ClickThrough Rates
Purchase history

3. What does BIG DATA mean for you?
Product details
10M item
meta-data
User behaviour
20M user
meta-data
1M items 10 parameters
per item
x
2M unique
visitors
10 parameters
per visitor
x
Interactions
popular categories
geolocation
User
contextual
data
integration
Item
contextual
data
Catalogue extension

4. What does BIG DATA mean for you?
5/26/2014

5. Methods of collecting and distributing user data
COLLECT and REPORT aggregated
data of your visitors
USE a RECOMMENDATION system
TRACK each visitor individually

6. How can it be used for business purposes?
Insight into classified Big Data
Degreeofinsight
1st click 2nd click 3rd click 1 week 1 month 1 year
Tracking & data collection
Data analysis
Adequate business
response
„Traditional” reactive marketing
Real-time personalization
Item-to-item
reco
Price range
Context
Device

7. How does personalization work?
𝐋 = (𝒖,𝒊)∈𝑻𝒓𝒂𝒊𝒏 𝒓 𝒖,𝒊 − 𝒓 𝒖,𝒊
𝟐
+ 𝝀 𝑼 𝒖=𝟏
𝑺 𝑼
𝑷 𝒖
𝟐+𝝀 𝑰 𝒊=𝟏
𝑺 𝑰
𝑸𝒊
𝟐
Recommendation techniques
Content based filtering
Collaborative filtering
 Recommends products that are liked
by users that have similar taste as the
current user
 Similarity between users is calculated
using the transaction history of users
 Domain independent
Recommends
additional products
with similar
properties

8. 1 4 3
4
4 4
4
2
1.4
-0.2
0.8
0.5
-1.3
-0.4 1.6
-0.1 0.5
0.3
1.2 -0.51.1 -0.4
1.2 0.9
0.4 -0.4
1.2 -0.3
1.3
-0.1
0.9
0.4
1.1 -0.2
1.5
0.0
1.1 0.8
-1.2
-0.3
1.2 0.9
1.6
0.11.5
0.0
0.5 -0.3
-1.1
-0.2
0.4 -0.20.5 -0.1
0.6
0.2
P
Q
R

9. 1 4 3
4
4 4
4
2
1.5
-1.0
2.1
0.8
1.0
1.6 1.8
0.7 1.6
0.0
1.4 1.1
0.9 1.9
2.5 -0.3
P
Q
R
3.3 2.4
-0.5 3.5 1.5
1.14.9

10. What type of data can be used for recommendations?
COLLABORATIVE
FILTERING
CONTENT-BASED
FILTERING
CONTEXT
AWARENESS
SOCIAL
RECOMMENDATIONS

11. Personalized User Journeys – Understand your
users and exploit the potential in BIG DATA
• Predicting not just the primary, but the secondary, tertiary, etc. interests
• Apart from history and behavior, focusing on the current context
Based on Interest Seasonality
Ad Replies Holidays
Searches Continuous
Devices used Working hours
Last activity peak
Every 3 months,
during weekends

12.  More user action and better user experience
impact on your market position and revenue
 Generate from 3rd
additional party
revenues placements
 Optimize your marketing spending on ad
networks by personalized banners and placements
How can you monetize from recommendations?

13. Thank you for your attention!
DomonkosTikk, PhD
Founder, CEO, CSO
Email: domonkos.tikk@gravityrd.com
hu.linkedin.com/in/domonkostikk/
Q&A