A presentation about scientific recommender systems from the seminarphase of the project group PUSHPIN at the University of Paderborn http://pgpushpin.wordpress.com/

1. Scientific Recommender Systems
Jan Petertonkoker
January 12th, 2012
Scientific Recommender Systems 1

2. Contents
Contents
1. Motivation (Examples)
2. Recommender Systems
3. Categories of Recommender Systems
3.1 Content-based Recommender: TF-IDF
3.2 Collaborative Recommender: Apache Mahout
3.3 Hybrid Recommender: SciPlore
4. Visualizations (Prototype)
5. Conclusion
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3. Motivation
Motivation
Example: Amazon
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4. Motivation
Motivation
Example: Twitter
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5. Recommender Systems
Recommender Systems
u :C ×S →R
C - set of all users
S - set of all items
R - totally ordered set, which describes the usefulness of the
items to the respective user
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6. Categories of Recommender Systems
Categories of Recommender Systems
content-based: items are recommended that are similar to
items the user liked in the past
collaborative: items are recommended that people liked that
are similar to the user (similar taste/preferences)
hybrid: a combination of content-based and collaborative
recommendation approaches
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7. Categories of Recommender Systems
Content-based Recommender Systems
utility u(c, s) of an item s is estimated with the help of the
utilities u(c, si ) of all items si ∈ S that user c already rated
that are similar to item s
similarity between items is calculated according to their
attributes
user and item profiles
common problems
limited content analysis
overspecialization
new user problem
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8. Categories of Recommender Systems
Content-based Recommender: TF-IDF
N - total number of documents in the system
keyword ki appears in ni of the documents
fi,j denotes the number of times a certain keyword ki appears
in a document dj
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9. Categories of Recommender Systems
Content-based Recommender: TF-IDF
N - total number of documents in the system
keyword ki appears in ni of the documents
fi,j denotes the number of times a certain keyword ki appears
in a document dj
Term Frequency
fi,j
TFi,j = maxz fz,j
maximum in the denominator calculated over the frequencies
of all keywords kz that appear in document dj
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10. Categories of Recommender Systems
Content-based Recommender: TF-IDF
N - total number of documents in the system
keyword ki appears in ni of the documents
fi,j denotes the number of times a certain keyword ki appears
in a document dj
Term Frequency
fi,j
TFi,j = maxz fz,j
maximum in the denominator calculated over the frequencies
of all keywords kz that appear in document dj
Inverse Document Frequency
N
for a keyword ki : IDFi = log ni
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11. Categories of Recommender Systems
Content-based Recommender: TF-IDF
N - total number of documents in the system
keyword ki appears in ni of the documents
fi,j denotes the number of times a certain keyword ki appears
in a document dj
Term Frequency
fi,j
TFi,j = maxz fz,j
maximum in the denominator calculated over the frequencies
of all keywords kz that appear in document dj
Inverse Document Frequency
N
for a keyword ki : IDFi = log ni
TF-IDF
wi,j = TFi,j × IDFi
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12. Categories of Recommender Systems
Collaborative Recommender Systems
utility u(c, s) of an item s is estimated with the help of the
utilities u(ci , s) assigned by users ci ∈ C that are similar to
user c.
common problems
new user/item problem
cold start
sparsity
scalability
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13. Categories of Recommender Systems
Collaborative Recommender: Apache Mahout (1)
provides a ”toolbox” to create collaborative recommender
systems
input
user (long), item (long), preference (double)
1, 111, 2.5
data model
input from different file formats, database
increase performance with specific data structures
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14. Categories of Recommender Systems
Collaborative Recommender: Apache Mahout (2)
user-based recommender
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15. Categories of Recommender Systems
Collaborative Recommender: Apache Mahout (2)
user-based recommender
item-based recommender
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16. Categories of Recommender Systems
Collaborative Recommender: Apache Mahout (3)
similarity measures
pearson correlation (cosine similarity)
euclidean distance
spearman correlation
log-likelihood
...
slope-one recommender
other experimental recommender implementations
e.g. cluster-based
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17. Categories of Recommender Systems
Hybrid Recommender Systems
combination of content-based and collaborative methods
seperate content-based and collaborative recommender
systems; results get combined somehow
collaborative recommender system with some added aspects of
content-based methods
content-based recommender system with some added aspects
of collaborative methods
a single recommender system which unifies content-based and
collaborative methods from the beginning
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18. Categories of Recommender Systems
Hybrid Recommender: SciPlore
SciPlore Overview
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19. Visualizations (Prototype)
Visualizations (Prototype)
several recommenders based on given database
visualizations for explaining recommendations
Live Presentation
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20. Conclusion
Summary
utility function
categories of recommender systems
content-based
collaborative
hybrid
implementation with Apache Mahout
possible visualizations
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21. Conclusion
Questions?
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22. References
References
Apache Mahout: Scalable machine learning and data mining.
http://mahout.apache.org/ - accessed on 6th January 2012
SciPlore: Exploring Science. http://www.sciplore.org -
accessed on 6th January 2012
G Adomavicius and A Tuzhilin. Toward the next generation of
recommender systems: a survey of the state-of-the-art and
possible extensions. IEEE Transactions on Knowledge and
Data Engineering, 17(6):734-749, 2005
B Gipp, J Beel and C Hentschel. Scienstein: A research paper
recommender system, volume 301, pages 309-315. IEEE, 2009
Sean Owen, Robin Anil, Ted Dunning and Ellen Friedman.
Mahout in Action, 2011
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