Presented at HetRec 2011 http://ir.ii.uam.es/hetrec2011/

1. Outline
Movie Recommendation
Folksonomies
Our Approach
Experiments
Conclusion & Future Work
Personalizing Tags: A Folksonomy-
like Approach for Recommending Movies
Alan Said Benjamin Kille Ernesto W. De Luca
Sahin Albayrak
{alan, kille, deluca, sahin}@dai-lab.de
DAI-Lab
TU-Berlin
HetRec, 2011
HetRec2011 :: Said, Kille, De Luca, Albayrak Personalizing Tags 1 / 18

2. Outline
Movie Recommendation
Folksonomies
Our Approach
Experiments
Conclusion & Future Work
Outline
Movie Recommendation
Folksonomies
Our Approach
Experiments
Conclusion & Future Work
HetRec2011 :: Said, Kille, De Luca, Albayrak Personalizing Tags 2 / 18

3. Outline
Movie Recommendation
Folksonomies
Our Approach
Experiments
Conclusion & Future Work
Abstract
Problem: How to simply use semantic data (tags, genres, etc.) in
usage-based collaborative filtering?
Aim: To provide a basic model of hybridization without adding
algorithmic complexity to a collaborative filtering recommender
system.
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4. Outline
Movie Recommendation
Folksonomies
Our Approach
Experiments
Conclusion & Future Work
Movie Recommendation
Traditional approach: Use users’ rating to find nearest
neighbors/latent factors/etc.
HetRec2011 :: Said, Kille, De Luca, Albayrak Personalizing Tags 4 / 18

5. Outline
Movie Recommendation
Folksonomies
Our Approach
Experiments
Conclusion & Future Work
Movie Recommendation
Traditional approach: Use users’ rating to find nearest
neighbors/latent factors/etc.
Traditional hybrid approach: Combine two or more parallel
algorithms.
HetRec2011 :: Said, Kille, De Luca, Albayrak Personalizing Tags 4 / 18

6. Outline
Movie Recommendation
Folksonomies
Our Approach
Experiments
Conclusion & Future Work
Movie Recommendation
Traditional approach: Use users’ rating to find nearest
neighbors/latent factors/etc.
Traditional hybrid approach: Combine two or more parallel
algorithms.
Our Approach:
Combine several data sources prior to recommendation process
- uses one algorithm.
Keep implementational effort low - allow easy implementation
in existing system.
HetRec2011 :: Said, Kille, De Luca, Albayrak Personalizing Tags 4 / 18

7. Outline
Movie Recommendation
Folksonomies
Our Approach
Experiments
Conclusion & Future Work
Definition
Definition: the result of personal free tagging of information and
objects . . . for ones own retrieval
[Vander Wal, 2004]
Tags offer a short content-related description of items to which
they are assigned.
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8. Outline
Movie Recommendation
Folksonomies
Our Approach
Experiments
Conclusion & Future Work
Relevance?
So..how is this relevant to movie
recommendation?
HetRec2011 :: Said, Kille, De Luca, Albayrak Personalizing Tags 6 / 18

9. Outline
Movie Recommendation
Folksonomies
Our Approach
Experiments
Conclusion & Future Work
Relevance?
Our movies have tags, e.g. categorized with tags from five cate-
gories:
Moods
Places
Times
Intended Audiences
Plots
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10. Outline
Movie Recommendation
Folksonomies
Our Approach
Experiments
Conclusion & Future Work
Relevance?
HetRec2011 :: Said, Kille, De Luca, Albayrak Personalizing Tags 7 / 18

11. Outline
Movie Recommendation
Folksonomies
Our Approach
Experiments
Conclusion & Future Work
Not quite a folksonomy
We have a problem: Tags are not personalized - they are
given to movies by a set of experts
HetRec2011 :: Said, Kille, De Luca, Albayrak Personalizing Tags 8 / 18

12. Outline
Movie Recommendation
Folksonomies
Our Approach
Experiments
Conclusion & Future Work
Not quite a folksonomy
We have a problem: Tags are not personalized - they are
given to movies by a set of experts
We solve it: Tags are assigned ratings
HetRec2011 :: Said, Kille, De Luca, Albayrak Personalizing Tags 8 / 18

13. Outline
Movie Recommendation
Folksonomies
Our Approach
Experiments
Conclusion & Future Work
Personalizing Tags
For each user, calculate the
average rating for each tag
based on the rating given to
movies with each tag.
HetRec2011 :: Said, Kille, De Luca, Albayrak Personalizing Tags 9 / 18

14. Outline
Movie Recommendation
Folksonomies
Our Approach
Experiments
Conclusion & Future Work
Personalizing Tags
For each user, calculate the
average rating for each tag
based on the rating given to
movies with each tag.
Little added effort if made
at the time of the rating.
HetRec2011 :: Said, Kille, De Luca, Albayrak Personalizing Tags 9 / 18

15. Outline
Movie Recommendation
Folksonomies
Our Approach
Experiments
Conclusion & Future Work
Using tag ratings
Append tag ratings to the user-movie matrix:
HetRec2011 :: Said, Kille, De Luca, Albayrak Personalizing Tags 10 / 18

16. Outline
Movie Recommendation
Folksonomies
Our Approach
Experiments
Conclusion & Future Work
Dataset
www.moviepilot.de tag category # of elements % rating coverage
840 users Emotion 16 61.85
15, 613 movies Intended Audience 12 35.50
Place 763 75.39
33, 061 movie ratings
Plot 5,565 90.00
6, 580 tags
Time 224 64.02
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17. Outline
Movie Recommendation
Folksonomies
Our Approach
Experiments
Conclusion & Future Work
Sparsity
100%
98% 97,46% 97,37% 97,40% 97,11%
96,50%
96%
94%
Sparsity
92%
90%
88,59%
87,89%
88%
86%
84%
82%
ratings +emotion +audience +place +plot +time +all
HetRec2011 :: Said, Kille, De Luca, Albayrak Personalizing Tags 12 / 18

18. Outline
Movie Recommendation
Folksonomies
Our Approach
Experiments
Conclusion & Future Work
Recommender
Collaborative Filtering kNN
50-fold random cross validation
HetRec2011 :: Said, Kille, De Luca, Albayrak Personalizing Tags 13 / 18

19. Outline
Movie Recommendation
Folksonomies
Our Approach
Experiments
Conclusion & Future Work
Results
9,0E-5
2850%
8,0E-5
2500%
7,0E-5
Mean Average Precision
6,0E-5
5,0E-5
4,0E-5
3,0E-5
2,0E-5
1,0E-5 207% 296%
100% 162% 153%
0,0E+0
baseline emotion audience place plot time all
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20. Outline
Movie Recommendation
Folksonomies
Our Approach
Experiments
Conclusion & Future Work
Conclusion & Future Work
Conclusion
Simple additions to traditional algorithms generate large
improvements
Future Work
Combinations of tags and time
Tag-based recommendations for cold start users
HetRec2011 :: Said, Kille, De Luca, Albayrak Personalizing Tags 15 / 18

21. Outline
Movie Recommendation
Folksonomies
Our Approach
Experiments
Conclusion & Future Work
Thank you!
Questions?
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22. Outline
Movie Recommendation
Folksonomies
Our Approach
Experiments
Conclusion & Future Work
CaRR2012
2nd Workshop on Context-awareness 2nd Workshop on Context-awareness
in Retrieval and Recommendation in in Retrieval and Recommendation
in Conjunction with IUI 2012, Lisbon, Portugal
conjunction IUI 2012.
Content and Goals of CaRR 2012
Context-aware information is widely available in various ways and is be-
Submission deadline: Dec. 2011 coming more and more important for enhancing retrieval performance
and recommendation results. The current main issue to cope with is not
only recommending or retrieving the most relevant items and content,
but defining them ad hoc. Further relevant issues are personalizing and
When: February 14th, 2012 adapting the information and the way it is displayed to the user’s cur-
rent situation and interests. Ubiquitous computing furher provides new
means for capturing user feedback on items and providing information.
Where: Lisbon, Portugal The aim of the 2nd Workshop on Context-awareness in Retrieval and
Recommendation is to invite the community to discuss new creative
ways to handle context-awareness. Furthermore, the workshop aims
on exchanging new ideas between different communities involved in
URL: www.carr-workshop.org research, such as HCI, machine learning, information retrieval and rec-
ommendation.
Twitter: @CaRRws Important Dates (tentative)
n Submission: End of Dec 2012
Program Committe (tentative)
Omar Alonso • Linas Baltrunas • Li
n Notification: tbd Chen • Brijnesh-Johannes Jain •
n Camera Ready: tbd Dietmar Jannach • Alexandros
n Workshop: February 14, 2012 Karatzoglou • Carsten Kessler •
Antonio Krüger • Michael Kruppa
Further Information • Ulf Leser • Pasquale Lops • Till
nWeb: http://carr-workshop.org Plumbaum • Francesco Ricci •
nE-Mail: info@carr-workshop.org Markus Schedl (to be extended)
nTwitter: @CaRRws
Chairs
n Ernesto de Luca, TU Berlin
n Matthias Böhmer, DFKI
n Alan Said, TU Berlin
n Ed Chi, Google
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23. Outline
Movie Recommendation
Folksonomies
Our Approach
Experiments
Conclusion & Future Work
RecSysWiki
www.recsyswiki.com
HetRec2011 :: Said, Kille, De Luca, Albayrak Personalizing Tags 18 / 18