personalizing tags: a folksonomy-like approach for recommending movies

OutlineMovie Recommendation

FolksonomiesOur ApproachExperiments

Conclusion & Future Work

Personalizing Tags: A Folksonomy-like Approach for Recommending Movies

Alan Said Benjamin Kille Ernesto W. De LucaSahin Albayrak

{alan, kille, deluca, sahin}@dai-lab.deDAI-LabTU-Berlin

HetRec, 2011

HetRec2011 :: Said, Kille, De Luca, Albayrak Personalizing Tags 1 / 18




Outline

Movie Recommendation

Folksonomies

Our Approach

Experiments






Abstract

Problem: How to simply use semantic data (tags, genres, etc.) inusage-based collaborative filtering?

Aim: To provide a basic model of hybridization without addingalgorithmic complexity to a collaborative filtering recommendersystem.





Movie Recommendation

I Traditional approach: Use users’ rating to find nearestneighbors/latent factors/etc.

I Traditional hybrid approach: Combine two or more parallelalgorithms.

I Our Approach:I Combine several data sources prior to recommendation process

- uses one algorithm.I Keep implementational effort low - allow easy implementation

in existing system.





Definition

Definition: the result of personal free tagging of information andobjects . . . for ones own retrieval

[Vander Wal, 2004]

Tags offer a short content-related description of items to whichthey are assigned.





Relevance?

So..how is this relevant to movierecommendation?





Relevance?

Our movies have tags, e.g. categorized with tags from five cate-gories:

I Moods

I Places

I Times

I Intended Audiences

I Plots





Relevance?





Not quite a folksonomy

I We have a problem: Tags are not personalized - they aregiven to movies by a set of experts

I We solve it: Tags are assigned ratings





Personalizing Tags

I For each user, calculate theaverage rating for each tagbased on the rating given tomovies with each tag.

I Little added effort if madeat the time of the rating.





Using tag ratings

Append tag ratings to the user-movie matrix:





Dataset

I www.moviepilot.de

I 840 users

I 15, 613 movies

I 33, 061 movie ratings

I 6, 580 tags

tag category # of elements % rating coverage

Emotion 16 61.85

Intended Audience 12 35.50

Place 763 75.39

Plot 5,565 90.00

Time 224 64.02





Sparsity

97,46% 97,37% 97,40% 96,50%

88,59%

97,11%

87,89%

82%

84%

86%

88%

90%

92%

94%

96%

98%

100%

ratings +emotion +audience +place +plot +time +all

Spar

sity





Recommender

I Collaborative Filtering kNN

I 50-fold random cross validation





Results

100% 207% 162%

296%

2500%

153%

2850%

0,0E+0

1,0E-5

2,0E-5

3,0E-5

4,0E-5

5,0E-5

6,0E-5

7,0E-5

8,0E-5

9,0E-5

baseline emotion audience place plot time all

Me

an A

vera

ge P

reci

sio

n






I ConclusionI Simple additions to traditional algorithms generate large

improvements

I Future WorkI Combinations of tags and timeI Tag-based recommendations for cold start users





Thank you!

Questions?





CaRR2012

2nd Workshop on Context-awarenessin Retrieval and Recommendation inconjunction IUI 2012.

I Submission deadline: Dec. 2011

I When: February 14th, 2012

I Where: Lisbon, Portugal

I URL: www.carr-workshop.org

I Twitter: @CaRRws

Content and Goals of CaRR 2012Context-aware information is widely available in various ways and is be-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 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.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 research, such as HCI, machine learning, information retrieval and rec-ommendation.

2nd Workshop on Context-awareness in Retrieval and Recommendationin Conjunction with IUI 2012, Lisbon, Portugal

Important Dates (tentative) n Submission: End of Dec 2012 n Notification: tbd n Camera Ready: tbd n Workshop: February 14, 2012

Further Information n Web: http://carr-workshop.org n E-Mail: [email protected] n Twitter: @CaRRws

Chairs n Ernesto de Luca, TU Berlin n Matthias Böhmer, DFKI n Alan Said, TU Berlin n Ed Chi, Google

Program Committe (tentative)Omar Alonso • Linas Baltrunas • Li Chen • Brijnesh-Johannes Jain •

Dietmar Jannach • Alexandros Karatzoglou • Carsten Kessler • Antonio Krüger • Michael Kruppa • Ulf Leser • Pasquale Lops • Till Plumbaum • Francesco Ricci • Markus Schedl (to be extended)





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personalizing tags: a folksonomy-like approach for recommending movies

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