Asta Bäck gave a presentation at the Smart Interaction Mobile and Media seminar, 24.9.2013

1. Käyttäjä- ja käyttäytymistietojen
hyödyntäminen suositusjärjestelmissä
User and behavioural data in
recommendation systems
Asiakasseminaari 24.9.2013
Asta Bäck, VTT

2. 26.9.2013
2
Growth - Driven by Data
Linked data
Recommendations
Open data
Content
Own websites
and mobile apps
Gathering
Analysing
Learning
Acting

3. 26.9.2013
Trust
3
People find
more than they
expected
Service more
relevant and
easier to use
Higher
ARPU
Reduced
churn
Longer
visits
Willing to
return

4. 26.9.2013
What do consumers think
of recommendations?
4

5. 26.9.2013
5
Which Recommendation Methods to Use?
User 1
User 2
User 3
User 4
User 5
User 6
User 7
…
Item 1
5
4
1
Item 2
4
2
3
1
4
Item 3
4
1
Item 4
1
…
Wellbeing
Health
3
2
Collaborative filtering
Content based recommendations
Knowledge based recommendations
Contextual recommendations
Social recommendations
Hybrid recommendations

6. 26.9.2013
6
Technology for content and knowledge based recommendations
An example of TV-program synopsis:
(1960) Lyhytelokuvassa esitellään suomalaista taideteollisuutta.
Mukana ovat mm. Dora Jung, Kaj Franck ja Timo Sarpaneva.
taideteollisuus
Timo Sarpaneva
Inferred meanings of keywords:
http://www.yso.fi/onto/koko/p34662
Keyword extraction
Semantic analysis of keywords
http://dbpedia.org/resource/Timo_Sarpaneva
Labels in different languages:
konstindustri (sv), industrial art
(en)
Related concepts: käsi- ja
taideteollisuus, lasitaide,
muotoilu, taidekäsityö,
sisustustaide…
Broader concept: teollisuus
Semantic expansion
Entities: Person, Finnish
industrial designers…
Semantic recommendations

7. 26.9.2013
7
Semantic profiles and recommendations in SP3/Mediatutka
New content and
semantic content
enrichment
Location sensitive
proactive notifications
Recommendations
Semantic user profiles and
semantic enrichment of
user metadata

8. 26.9.2013
8
Recommendations Based on Collaborative Filtering
A generic self organizing method (UPCV), based on the
behavior of individual users and user groups:
no metadata; applicable to any item in any service
easily converted to a hybrid recommender
simple to understand and use; contains no secret sauce
UPCV is able to provide:
user-to-item recommendations (items for a user)
item-to-item recommendations (items similar to an item)
item-to-user recommendations (interested users for an item)
user-to-user recommendations (users with similar behavior)
Patents pending (FI, EPO, US)

9. 26.9.2013
9
Open Recommendation Initiative based on UPCV
Each party (service, user) owns all recommendation data related to it
Recommendation is based on all user activities, in any service
Customership belongs to services - not e.g. to Google!
Uncompromised privacy
UPCV recommendation engine:
Distributed architecture
Simple HTTP REST API
Small footprint
Easy deployment

10. 26.9.2013
VTT creates business from technology
10