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

Social Personalisation Workshop @ HT‘14

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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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Social Personalisation Workshop @ HT‘14

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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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Social Personalisation Workshop @ HT‘14

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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%

Social Personalisation Workshop @ HT‘14

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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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Social Personalisation Workshop @ HT‘14

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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.

Social Personalisation Workshop @ HT‘14

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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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Social Personalisation Workshop @ HT‘14

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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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