Post on 24-May-2015
description
Social Personalisation Workshop @ HT‘14
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. Christoph Trattner 1.9.2014 – PUC, Chile
Recommending Items in Social Tagging Systems using Tag and Time Information
Christoph TrattnerKnow-Center
ctrattner@know-center.at
@Graz University of Technology, Austria
Social Personalisation Workshop @ HT‘14
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. Christoph Trattner 1.9.2014 – PUC, Chile
Emanuel Lacicelacic@know-center.atTUGAustria
Paul Seitlingerpaul.seitlinger@tugraz.atTUGAustria
Denis Parradparra@ing.puc.clPUCChile
Thanks to
Dominik Kowalddkowald@know-center.atKnow-CenterAustria
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. Christoph Trattner 1.9.2014 – PUC, Chile
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
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. Christoph Trattner 1.9.2014 – PUC, Chile 4
Problem:Predict/Recommend items in social tagging systems people (might be) interested in to read
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. Christoph Trattner 1.9.2014 – PUC, Chile
Why are we doing this?
Basically, to help the user in exploring an overloaded information space more efficiently
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. Christoph Trattner 1.9.2014 – PUC, Chile
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.
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. Christoph Trattner 1.9.2014 – PUC, Chile
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
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. Christoph Trattner 1.9.2014 – PUC, Chile
Which function fits better to model the drift of interests in social tagging
systems?
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. Christoph Trattner 1.9.2014 – PUC, Chile
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
Social Personalisation Workshop @ HT‘14
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. Christoph Trattner 1.9.2014 – PUC, Chile
Empirical Analysis: BibSonomy (2)
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Exponential distributionR² = 31%
Power distributionR² = 89%
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. Christoph Trattner 1.9.2014 – PUC, Chile
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:
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. Christoph Trattner 1.9.2014 – PUC, Chile
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.
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. Christoph Trattner 1.9.2014 – PUC, Chile
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:
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. Christoph Trattner 1.9.2014 – PUC, Chile
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 1.9.2014 – PUC, Chile
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 1.9.2014 – PUC, Chile
Results: nDCG plots
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CIRTT reaches the highest level of accuracy
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. Christoph Trattner 1.9.2014 – PUC, Chile
Results: Recall plots
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CIRTT reaches the highest level of accuracy
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. Christoph Trattner 1.9.2014 – PUC, Chile
...ok that‘s basically it
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. Christoph Trattner 1.9.2014 – PUC, Chile
What are we currently working on?
http://recsium.know-center.tugraz.at/recsium/
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. Christoph Trattner 1.9.2014 – PUC, Chile
Thank you!
Christoph Trattner
Email: ctrattner@know-center.atWeb: christophtrattner.info
Twitter: @ctrattner
Sponsors:
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. Christoph Trattner 1.9.2014 – PUC, Chile
Code and Framework
Code and framework:
https://github.com/learning-layers/TagRec/
Questions?
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