Social Personalisation Workshop @ HT‘14 
1 
Recommending Items in Social Tagging 
Systems using Tag and Time Information 
...
Social Personalisation Workshop @ HT‘14 
2 
Emanuel Lacic 
elacic@know-center.at 
TUG 
Austria 
Paul Seitlinger 
paul.seit...
Social Personalisation Workshop @ HT‘14 
3 
What will this talk be about? 
• Social tags 
• Temporal usage patterns of soc...
Social Personalisation Workshop @ HT‘14 
4 
Problem: 
Predict/Recommend items in social 
tagging systems people (might be)...
Social Personalisation Workshop @ HT‘14 
5 
Why are we doing this? 
Basically, to help the user in exploring an overloaded...
Social Personalisation Workshop @ HT‘14 
6 
Current approaches out there?! 
... aaaa looot on the tag prediction problem.....
Social Personalisation Workshop @ HT‘14 
7 
Temporal Tag Usage Patterns 
Usually the interests of users drift over time  ...
Social Personalisation Workshop @ HT‘14 
8 
Which function fits better to model the 
drift of interests in social tagging ...
Social Personalisation Workshop @ HT‘14 
• Linear distribution with log-scale 
9 
Empirical Analysis: BibSonomy (1) 
. Chr...
Social Personalisation Workshop @ HT‘14 
10 
Empirical Analysis: BibSonomy (2) 
. Christoph Trattner 1.9.2014 – PUC, Chile...
Social Personalisation Workshop @ HT‘14 
11 
Our Approach 
Base-Level learning (BLL) equation - part of ACT-R 
model Ander...
Social Personalisation Workshop @ HT‘14 
12 
Previous research (tag prediction) 
Trattner, C., Kowald, D., Seitlinger, P.,...
Social Personalisation Workshop @ HT‘14 
13 
Our Approach (2) 
= CIRTT  2 main steps 
First step: 
– User-based Collabora...
Social Personalisation Workshop @ HT‘14 
IR metrics: nDCG@20, MAP@20, Recall@20, Diversity and 
14 
How does it perform? 
...
Social Personalisation Workshop @ HT‘14 
15 
Baseline Methods 
• Most Popular (MP) 
• User-based Collaborative Filtering (...
Social Personalisation Workshop @ HT‘14 
16 
Results: nDCG plots 
. Christoph Trattner 1.9.2014 – PUC, Chile 
16 
CIRTT re...
Social Personalisation Workshop @ HT‘14 
17 
Results: Recall plots 
. Christoph Trattner 1.9.2014 – PUC, Chile 
17 
CIRTT ...
Social Personalisation Workshop @ HT‘14 
18 
...ok that‘s basically it  
. Christoph Trattner 1.9.2014 – PUC, Chile
Social Personalisation Workshop @ HT‘14 
19 
What are we currently working on? 
http://recsium.know-center.tugraz.at/recsi...
Social Personalisation Workshop @ HT‘14 
20 
Thank you! 
Christoph Trattner 
Email: ctrattner@know-center.at 
Web: christo...
Social Personalisation Workshop @ HT‘14 
21 
Code and Framework 
Code and framework: 
https://github.com/learning-layers/T...
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Recommending Items in Social Tagging Systems Using Tag and Time Information

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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

  1. 1. Social Personalisation Workshop @ HT‘14 1 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
  2. 2. Social Personalisation Workshop @ HT‘14 2 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
  3. 3. Social Personalisation Workshop @ HT‘14 3 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
  4. 4. Social Personalisation Workshop @ HT‘14 4 Problem: Predict/Recommend items in social tagging systems people (might be) interested in to read . Christoph Trattner 1.9.2014 – PUC, Chile 4
  5. 5. Social Personalisation Workshop @ HT‘14 5 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 5
  6. 6. Social Personalisation Workshop @ HT‘14 6 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
  7. 7. Social Personalisation Workshop @ HT‘14 7 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 7
  8. 8. Social Personalisation Workshop @ HT‘14 8 Which function fits better to model the drift of interests in social tagging systems? . Christoph Trattner 1.9.2014 – PUC, Chile 8
  9. 9. Social Personalisation Workshop @ HT‘14 • Linear distribution with log-scale 9 Empirical Analysis: BibSonomy (1) . Christoph Trattner 1.9.2014 – PUC, Chile 9 on Y-axis  exponential function • Linear distribution with log-scale on X- and Y-axes  power function
  10. 10. Social Personalisation Workshop @ HT‘14 10 Empirical Analysis: BibSonomy (2) . Christoph Trattner 1.9.2014 – PUC, Chile 10 Exponential distribution R² = 31% Power distribution R² = 89%
  11. 11. Social Personalisation Workshop @ HT‘14 11 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 11
  12. 12. Social Personalisation Workshop @ HT‘14 12 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
  13. 13. Social Personalisation Workshop @ HT‘14 13 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 13
  14. 14. Social Personalisation Workshop @ HT‘14 IR metrics: nDCG@20, MAP@20, Recall@20, Diversity and 14 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 14
  15. 15. Social Personalisation Workshop @ HT‘14 15 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) . Christoph Trattner 1.9.2014 – PUC, Chile 15
  16. 16. Social Personalisation Workshop @ HT‘14 16 Results: nDCG plots . Christoph Trattner 1.9.2014 – PUC, Chile 16 CIRTT reaches the highest level of accuracy
  17. 17. Social Personalisation Workshop @ HT‘14 17 Results: Recall plots . Christoph Trattner 1.9.2014 – PUC, Chile 17 CIRTT reaches the highest level of accuracy
  18. 18. Social Personalisation Workshop @ HT‘14 18 ...ok that‘s basically it  . Christoph Trattner 1.9.2014 – PUC, Chile
  19. 19. Social Personalisation Workshop @ HT‘14 19 What are we currently working on? http://recsium.know-center.tugraz.at/recsium/ . Christoph Trattner 1.9.2014 – PUC, Chile
  20. 20. Social Personalisation Workshop @ HT‘14 20 Thank you! Christoph Trattner Email: ctrattner@know-center.at Web: christophtrattner.info Twitter: @ctrattner Sponsors: . Christoph Trattner 1.9.2014 – PUC, Chile
  21. 21. Social Personalisation Workshop @ HT‘14 21 Code and Framework Code and framework: https://github.com/learning-layers/TagRec/ Questions? . Christoph Trattner 1.9.2014 – PUC, Chile 21

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