from search to predictions in tagged information spaces
DESCRIPTION
Tagging gained tremendously in popularity over past few years. When looking into the literature of tagging we find a lot of work regarding people's tagging motivation, their behavior, models that describe the folksonomy generation process, emergent semantic structures, etc., but interestingly we find quite little research showing the value of tags for searching an overloaded information space. Furthermore, there is lot of literature on the tag or item prediction problem, but interestingly almost all of them lookat the issue from a data-driven perspective. To bridge this gap in the literature, we have conducted several in-depth studies in the past showing the value of tags for lookup and exploratory search. We looked at the problem from a network theoretic and interface perspective and we will show how useful tags are for searching. Furthermore, we reviewed literature on memory processes from cognitive science and have invented a number of novel recommender algorithms based on the ACT-R and MINERVA2 theory. We will show that these approaches can not only predict tags and items extremely well, but also reveal how these models can help in explaining the recommendation processes better than current approaches.TRANSCRIPT
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
From Search to Predictions in Tagged Information Spaces
Christoph TrattnerKnow-Center
@Graz University of Technology, Austria
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Before start in this presentation I will talk a bit about myself, my background…
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Where do I come from (Austria)?
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Graz
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
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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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
My team
2 Post-Docs, 5 Pre-Docs (2 more to join soon )
2 MSc student2 BSc student
DI. Dieter Theiler
DI. Dominik Kowald
Dr. Peter Kraker
Mag. Sebastian Dennerlein
Dr. Elisabeth Lex
Mag. MatthiasRella
DI. Emanuel Lacic
DI. Ilire Hasani
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Thanks to my Collaborators
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
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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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Some industry partners...
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
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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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
The Projects
Project 1: Mendeley – UK Startup (recently acquired by Elsevier): Interested in the problem of hirarchical concept-based search in tagged information spaces.
Project 2: Tallinn University– Interested in the problem of recommending tags and items in tagged information spaces.
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Ok, let’s start….
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Project 1
Mendeley – UK Startup (recently acquired by Elsevier): Interested in the problem of hierarchical concept-based search.
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Research Question 1:
What kind of meta-data is more useful for search in information systems - tags or keywords?
Externals involved: • Mendeley, London, UK
Helic, D., Körner, C., Granitzer, M., Strohmaier, M. and Trattner, C. 2012. Navigational Efficiency of Broad vs. Narrow Folksonomies. In Proceedings of the 23rd ACM Conference on Hypertext and Social Media (HT 2012), ACM, New York, NY, USA, pp. 63-72.
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Mendeley
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
We
Keywords
Tags
Mendeley Desktop
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Task
What is the best way to extract hirarchies from tagged information spaces? What is more useful for navigation – keyword or tag hierarchies?
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Different types of hierarchy induction algorithms
Helic, D., Strohmaier, M., Trattner, C., Muhr M. and Lermann, K.: Pragmatic Evaluation of Folksonomies, In Proceedings of the 20th international conference on World Wide Web (WWW 2011), ACM, New York, NY, USA, 417-426, 2011.
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Issue (!!!)
...no literature on what type of hierarchy is best suited for searching...
D. J. Watts, P. S. Dodds, and M. E. J. Newman. Identity andsearch in social networks. Science, 296:1302–1305, 2002.
J. M. Kleinberg. Navigation in a small world. Nature,406(6798):845, August 2000.
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Stanley Milgram
A social psychologist Yale and Harvard University
Study on the Small World Problem,beyond well defined communities and relations(such as actors, scientists, …)
„An Experimental Study of the Small World Problem”
1933-1984
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Set Up
Target person: A Boston stockbroker
Three starting populations 100 “Nebraska stockholders” 96 “Nebraska random” 100 “Boston random”
Nebraska random
Nebraska stockholders
Boston stockbroker
Boston random
Target
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Results
How many of the starters would be able to establish contact with the target? 64 out of 296 reached the target
How many intermediaries would be required to link starters with the target? Well, that depends: the overall mean 5.2 links Through hometown: 6.1 links Through business: 4.6 links Boston group faster than Nebraska groups Nebraska stockholders not faster than Nebraska random
What form would the distribution of chain lengths take?
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Hierarchical decentralized searcher
InformationNetwork
Hierarchy
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Results
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Validation
We compared simulations with
human click trails of the online Game –
The Wiki Game (http://thewikigame.com/)
Contains 1,500,000
click trails of more
than 500,000 users with
(start; target) information.
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Hierachy Creation (1)
Two types of hierarchies were evaluated
1.) First type is based on our previous work Categorial Concepts:
Tags from Delicious Category labels from Wikipedia
Similarity GraphLatent Hierarchical Taxonomy
Wikipedia Category Label Dataset: 2,300,000 category labels,4,500,000 articles, 30,000,000 category label assignments
Delicious Tag Dataset: 440,000 tags, 580,000 articles and3,400,000 tag assignments
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Hierarchy Creation (2)
2.) Second type is based on the work of [Muchnik et al. 2007]
Muchnik, L., Itzhack, R., Solomon S. and Louzoun Y.: Self-emergence of knowledge trees: Extraction of the Wikipedia hierarchies, PHYSICAL REVIEW E 76, 016106 (2007)
Simple idea: Algorithm iterates through all links in the network and decides if that link is of a hierarchical type, in which case it remains in the network otherwise it is removed.
Directed link-network dataset of theEnglish-Wikipedia from February 2012.
All in all, the dataset includesaround 10,000,000 articles and around 250,000,000 links
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
ValidationHuman Searchers
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
...ok let‘s come back to the Mendeley „problem“...
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Tags
Are keyword hierarchies better for search than social tag hierarchies?
Keywords
Results: Our Greedy Navigator (= Simulator) needs on average 1-clickmore with keywords to reach the target node than with tags
Results:
With simulations we find that tag-based hierarchies are more efficient for navigation than keywords
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
...ok let‘s move on to some prediction stuff
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Project 2
Tallinn University – Interested in the problem of recommending items and tags to users in social tagging systems.
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Research Question 2:
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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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
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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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
To overcome that issue some smart people started to invent mechanisms that should help the user in applying tags, known as social tag recommender system based on:
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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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
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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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
MINERVA2
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Approach
Based on a Human cognition (derived from MINERVA2 [Kruschke et al.,
1992])
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Evaluation Wikipedia
p-core pruning (p = 14)
To finally measure to performance of our approach we split up our dataset in two
sub-sets 80% for training and 20% for testing Training
Precision, Recall, F1-score, MRR, MAP
As Baseline algorithm we have chosen Latent Dirichlet Allocation (LDA)
[Krestel et al. 2009]
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Results
Results:
3Layers reaches higher levels of estimate than the pure LDA approach.
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
ACT-R
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
ACT-R
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
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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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
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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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Empirical Analysis: BibSonomy (2)
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Exponential distributionR² = 31%
Power distributionR² = 89%
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Results:
Decay factor is better modeled as power-function rather than an ex-function
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Experiment 1: Predicting re-use of tags
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Results: Predicting re-use of tags
BLLAC
BLLMPU
GIRP
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Results: Recall / Precision
Results:
BLLAC performs fairly well in predicting the re-use of tags
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Experiment 2: Recommending Tags
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
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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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Results: Recall \ Precision
Results:
BLL approaches outperform current state-of-the-art tag recommender approaches.
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
...how about runtime?
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Results: Runtime
BLL+C needs only around 1s to generate tag-recommendations for 5,500 users in BibSonomy
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Results: Runtime
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
...predicting (re-ranking) items with ACT-R
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
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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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
How does it perform?
3 freely-available folksonomy datasets– BibSonomy (~ 340,000 tag assignments)– CiteULike (~ 100.000 tag assignments)– MovieLens (~ 100.000 tag assignments)
Original datasets (no p-core pruning) Doerfel et al. (2013)
80/20 split (for each user 20% most recent bookmarks/posts in test-set, rest in training-set)
IR metrics: nDCG@20, MAP@20, Recall@20, Diversity and User Coverage
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Baseline Methods
• Most Popular (MP)
• User-based Collaborative Filtering (CF)
• Two alternative approaches based on tag and time information– Zheng et al. (2011) exponential function– Huang et al. (2014) linear function
(remember: our CIRTT uses a power function)
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Results: nDCG plots
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CIRTT reaches the highest level of accuracy
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Results: Recall plots
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CIRTT reaches the highest level of accuracy
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Results
Results:
CIRTT works quite well compared to the current state-of-the-art in tag-based item recommender systems
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
What are we...
...currently working on...
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
MINERVA2 + ACT-R
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Time in Semantic vs. Lexical Memory
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Topical vs. Lexical shift in time
Tags
Topics
Results:
Topical shift in time is less pronounced than lexical shift
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Results: Recall / Precision
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Describer vs. Categorizer
M. Strohmaier, C. Koerner, and R. Kern. Understanding why users tag: A survey of tagging motivation literature and results from an empirical study. Journal of Web Semantics, 17:1–11, 2012.
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Results: Categorizer vs. Describer
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
... ok that‘s basically it
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Code and Framework
https://github.com/learning-layers/TagRec/
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. Christoph Trattner 30.10.2014 – Yahoo! Labs, Barcelona
Thank you!
Christoph Trattner
Email: [email protected]: christophtrattner.info
Twitter: @ctrattner
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