Cognitive Models in Recommender Systems

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. Christoph Trattner 28.1.2015 – Yahoo!, Trondheim
Cognitive Models in Recommender
Systems
Christoph Trattner
Know-Center & NTNU
ctrattner@know-center.at
@Graz University of Technology, Austria

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Before start in this presentation I will talk a bit about
myself, my background…

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Where do I come from (Austria)?

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Graz

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Academic Back-Ground?
 Studied Computer Science at Graz University of
Technology & University of Pittsburgh
 Worked since 2009 as scientific researcher at the KMI &
IICM (BSc 2008, MSc 2009)
 My PhD thesis was on the Search & Navigation in Social
Tagging Systems (defended 2012)
 Since Feb. 2013 @ Know-Center
 Leading the Social Computing Area
 At TUG:
 WebScience
 Semantic Technologies

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My team
2 Post-Docs, 5 Pre-Docs (2 more to join soon )
2 MSc student
2 BSc student
DI. Dieter
Theiler
DI. Dominik
Kowald
Dr. Peter
Kraker
Mag. Sebastian
Dennerlein
Dr. Elisabeth
Lex
Mag. Matthias
Rella
DI. Emanuel
Lacic
DI. Ilire Hasani

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Thanks to my Collaborators

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What is my group doing?
… we research on novel methods and tools that exploit
social data to generate a greater value for the
individual, communities, companies and the society as
whole.
Our competences:
• Network & Web Science
• Science 2.0
• Predictive Modeling
• Social Network Analysis
• Information Quality Assessment
• User Modeling
• Machine Learning and Data Mining
• Collaborative Systems
Our Services:
• Social Analytics: Hub-, Expert -, Community -
, Influencer -, Information Flow-, Trend
(Event) Detection, etc.
• Information Quality Assessment
• Social & Location-based Recommander
Systems
• Customer Segmentation
• Social Systems Design

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Some industry partners...

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Current projects
BlancNoir - “Towards a Big Data recommender engine for offline
and online marketplaces”
I2F - “Towards a Social Media and Online Marketing Manager
Seminar”
Automation-X - “Towards a scalable Graph-based Visual search
solution”
Styria - “Towards a scalable crowd-based hierarchical cluster
labeling approach for willhaben.at”
TripRebel - “Towards an engaging hybrid hotel recommender
solution for triprebel.com”
CDS - “Towards a scalable Entity & Graph-based Visual search
solution for cds.at”
Exthex - “Towards an efficient viral social media marketing
champagne in Facebook and Twitter”

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The Projects
Project: Tallinn University – Interested in the problem of
recommending tags and items to users in social information
systems.

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Ok, let’s start….

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Research Question:
To what extent is human cognition theory applicable to
the problem of predicting tags and items to users?
Externals involved:
• PUC - Chile, UFCG – Brazil

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What are social tags?
Where can we find them?

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Why are social tags good?

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 They help you to classify Web content better [Zubiaga 2012]
 They help people to navigate large knowledge repositories better
[Helic et al. 2012]
 They help people to search for information faster [Trattner et al. 2012]
However, there is an issue with social tags…
People are typically lazy to apply social tags(!!)
Zubiaga, A. (2012). Harnessing Folksonomies for Resource Classification. arXiv preprint arXiv:1204.6521.
Helic, D., Körner, C., Granitzer, M., Strohmaier, M., & Trattner, C. (2012, June). Navigational efficiency of broad vs.
narrow folksonomies. In Proceedings of the 23rd ACM conference on Hypertext and social media (pp. 63-72). ACM.
Trattner, C., Lin, Y. L., Parra, D., Yue, Z., Real, W., & Brusilovsky, P. (2012, June). Evaluating tag-based information
access in image collections. In Proceedings of the 23rd ACM conference on Hypertext and social media (pp. 113-
122). ACM.
Motivation

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To overcome that issue some smart people started to invent mechanisms that
should help the user in applying tags:
 Collaborative Filtering
 User based- and item-based CF [Marinho et al. 2008]
 Matrix Factorization
 FM, PITF [Rendle et al. 2010, 2011, 2012]
 Graph Structures
 Adapted PageRank and FolkRank [Hotho et al. 2006]
 Topic Models
 Latent Dirichlet Allocation (LDA) [Krestel et al. 2009, 2010, 2011]
Motivation

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Why do we need cognitive models in
recommender systems?

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Why do we need cognitive models?
First answer: We do not like data data driven approaches…
Me: OK
Second answer: We can understand things better…
…why is something happening and how…

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ACT-R

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Cognitive Models
• In principle, there are several approaches to model
cognitive processes (memory, speech, ...) in the
human brain
• Most popular one: ACT-R (Adaptive
Control of Thought-Rational) theory
by J.R. Anderson (1998)
• American Psychologist (CMU)
J.R. Anderson
CMU

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ACT-R

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Interestingly, when looking into the literatur of tagging
systems - temporal processes are typically modeled
with an exponential function...
D. Yin, L. Hong, and B. D. Davison. Exploiting session-like behaviors in tag prediction. In
Proceedings of the 20th international conference companion on World wide web, pages
167–168. ACM, 2011.
L. Zhang, J. Tang, and M. Zhang. Integrating temporal usage pattern into personalized tag
prediction. In Web Technologies and Applications, pages 354–365. Springer, 2012

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Empirical Analysis: BibSonomy (1)
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 Linear distribution with log-
scale on Y-axis 
exponential function
 Linear distribution with log-
scale on X- and Y-axes 
power function

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Empirical Analysis: BibSonomy (2)
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Exponential distribution
R² = 31%
Power distribution
R² = 89%

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Results:
Decay factor is better modeled as
power-function rather than an ex-
function

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Experiment 1: Predicting re-use of tags

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Results: Predicting re-use of tags
BLLAC
BLL
MPU
GIRP

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Results: Recall / Precision
Results:
BLLAC performs fairly well in
predicting the re-use of tags

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Experiment 2: Recommending Tags

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Results: Recall-Precision plots
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 The time-depended
approaches outperform the
state-of-the-art
 BLL+MPr reaches the
highest level of accuracy
CiteULike

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Results: Recall/Precision
Results:
BLL approaches outperform current
state-of-the-art tag recommender
approaches.

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...how about runtime?

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Results: Runtime
BLL+C needs only around 1s to generate tag-
recommendations for 5,500 users in BibSonomy

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Results: Runtime

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...predicting items with ACT-R

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Our Approach
= CIRTT  2 main steps
First step:
– User-based Collaborative Filtering (CF) to get
candidate items of similar users
Second step:
– Item-based CF to rank these candidate items using
the BLL equation to integrate tag and time
information:
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Example
big fruit (t=10d)
small fruit (t=10d)
big fruit (t=2d)
small fruit (t=10d)
Recommendation:
Rank@1 =
Rank@2 =

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What are the results?
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Results: nDCG plots
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CIRTT reaches the highest level of accuracy

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Results
Results:
CIRTT works quite well compared to
the current state-of-the-art in tag-
based item recommender systems

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... ok that‘s basically it 

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Code & Framework
https://github.com/learning-layers/TagRec/

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Thank you!
Christoph Trattner
Email: trattner.christoph@gmail.com
Web: christophtrattner.info
Twitter: @ctrattner
Sponsors:

Cognitive Models in Recommender Systems

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