cognitive models in recommender systems

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. Christoph Trattner 28.1.2015 – Yahoo!, Trondheim

Cognitive Models in Recommender

Systems

Christoph TrattnerKnow-Center & NTNU

[email protected]

@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

KowaldDr. Peter

KrakerMag. 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

BLLMPU

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/

https://github.com/learning-layers/TagRec/

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Thank you!

Christoph Trattner

Email: [email protected]

Web: christophtrattner.info

Twitter: @ctrattner

Sponsors:

cognitive models in recommender systems

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