In this work we present a novel item recommendation ap- proach that aims at improving Collaborative Filtering (CF) in social tagging systems using the information about tags and time. Our algorithm follows a two-step approach, where in the first step a potentially interesting candidate item-set is found using user-based CF and in the second step this can- didate item-set is ranked using item-based CF. Within this ranking step we integrate the information of tag usage and time using the Base-Level Learning (BLL) equation com- ing from human memory theory that is used to determine the reuse-probability of words and tags using a power-law forgetting function. As the results of our extensive evaluation conducted on data- sets gathered from three social tagging systems (BibSonomy, CiteULike and MovieLens) show, the usage of tag-based and time information via the BLL equation also helps to improve the ranking and recommendation process of items and thus, can be used to realize an effective item recommender that outperforms two alternative algorithms which also exploit time and tag-based information.

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Recommending Items in Social Tagging
Systems using Tag and Time Information
Christoph Trattner
Know-Center
ctrattner@know-center.at
@Graz University of Technology, Austria
. Christoph Trattner 1.9.2014 – PUC, Chile

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Emanuel Lacic
elacic@know-center.at
TUG
Austria
Paul Seitlinger
paul.seitlinger@tugraz.at
TUG
Austria
. Christoph Trattner 1.9.2014 – PUC, Chile
Denis Parra
dparra@ing.puc.cl
PUC
Chile
Thanks to
Dominik Kowald
dkowald@know-center.at
Know-Center
Austria

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What will this talk be about?
• Social tags
• Temporal usage patterns of social tags
• Recommending items in social tagging systems
• An equation derived from human memory theory
. Christoph Trattner 1.9.2014 – PUC, Chile

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Problem:
Predict/Recommend items in social
tagging systems people (might be)
interested in to read
. Christoph Trattner 1.9.2014 – PUC, Chile 4

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Why are we doing this?
Basically, to help the user in exploring an overloaded
information space more efficiently
. Christoph Trattner 1.9.2014 – PUC, Chile
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Current approaches out there?!
... aaaa looot on the tag prediction problem...
Marinho et al. (2012)
...but relativly little on recommending items to people in
social tagging systems...
L. Balby Marinho, A. Hotho, R. Jäschke, A. Nanopoulos, S. Rendle, L. Schmidt-Thieme, G.
Stumme, and P. Symeonidis. Recommender Systems for Social Tagging Systems. SpringerBriefs
in Electrical and Computer Engineering. Springer, Feb. 2012.
. Christoph Trattner 1.9.2014 – PUC, Chile

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Temporal Tag Usage Patterns
Usually the interests of users drift over time  and so does
their tagging behavior
The work of e.g., Zhang et al. (2012) shows that the time
component is important for social tagging
– Models the time component using an exponential function
Empirical research on human memory (Anderson &
Schooler, 1991) showed that the reuse-probability of a
word (= tag) depends on its usage-frequency and
recency in the past
– Models the time component using a power function
. Christoph Trattner 1.9.2014 – PUC, Chile
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Which function fits better to model the
drift of interests in social tagging
systems?
. Christoph Trattner 1.9.2014 – PUC, Chile
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• Linear distribution with log-scale
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Empirical Analysis: BibSonomy (1)
. Christoph Trattner 1.9.2014 – PUC, Chile
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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)
. Christoph Trattner 1.9.2014 – PUC, Chile
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Exponential distribution
R² = 31%
Power distribution
R² = 89%

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Our Approach
Base-Level learning (BLL) equation - part of ACT-R
model Anderson et al. (2004):
In previous work we have shown that this equation can be
used to build an effective tag recommender
Kowald et al. (2014), Trattner et al. (2014)
Adaption for item recommendation:
. Christoph Trattner 1.9.2014 – PUC, Chile
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Previous research (tag prediction)
Trattner, C., Kowald, D., Seitlinger, P., Kopeinik, S. and Ley, T.: Modeling Activation Processes in Human Memory to
Predict the Reuse of Tags, Journal of Web Science, 2014. (under review)
Kowald, D., Seitlinger, P., Trattner, C. and Ley, T.: Long Time No See: The Probability of Reusing Tags as a Function of
Frequency and Recency, In Proceedings of the ACM World Wide Web Conference (WWW 2014), ACM, New York, NY,
2014.
. Christoph Trattner 1.9.2014 – PUC, Chile

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Our Approach (2)
= 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:
. Christoph Trattner 1.9.2014 – PUC, Chile
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IR metrics: nDCG@20, MAP@20, Recall@20, Diversity and
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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)
User Coverage
. Christoph Trattner 1.9.2014 – PUC, Chile
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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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Results: nDCG plots
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CIRTT reaches the highest level of accuracy

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Results: Recall plots
. Christoph Trattner 1.9.2014 – PUC, Chile
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CIRTT reaches the highest level of accuracy

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...ok that‘s basically it 
. Christoph Trattner 1.9.2014 – PUC, Chile

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What are we currently working on?
http://recsium.know-center.tugraz.at/recsium/
. Christoph Trattner 1.9.2014 – PUC, Chile

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Thank you!
Christoph Trattner
Email: ctrattner@know-center.at
Web: christophtrattner.info
Twitter: @ctrattner
Sponsors:
. Christoph Trattner 1.9.2014 – PUC, Chile

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Code and Framework
Code and framework:
https://github.com/learning-layers/TagRec/
Questions?
. Christoph Trattner 1.9.2014 – PUC, Chile
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