Ukrainian Catholic University Faculty of Applied Sciences Data Science Master Program January 21st Abstract. Human navigation in information spaces has increasing importance in ever-growing data sources we possess. Therefore, an efficient navigation strategy would give a huge benefit to the satisfaction of human information needs. Often, the search space can be understood as a network and navigation can be seen as a walk on this network. Previous studies have shown that despite not knowing the global network structure people tend to be efficient at finding what they need. This is usually explained by the fact that people possess some background knowledge. In this work, we explore an adapted version of the network consisting of Wikipedia pages and links between them as well as human trails on it. The goal of our research is to find a procedure to label articles that are similar to a given one. Among others, this would lay a foundation for a recommender system for Wikipedia editors, which will suggest links from the given page to the related articles. Our work is, therefore, providing a basement for enhancing the Wikipedia navigation process making it more user-friendly.

1. Topological approach to Wikipedia
article recommendation
Author:
Maksym Opirskyi
opirskyi@ucu.edu.ua
Supervisor:
Petro Sarkanych
sarkanyp@coventry.ac.uk
1. Institute for Condensed Matter Physics: Lviv, UA
2. L4 Collaboration & Doctoral College for the Statistical Physics of
Complex Systems, Leipzig-Lorraine-Lviv-Coventry, Europe
3. Ukrainian Catholic University

2. Plan
● Introduction and motivation
● Problem statement and research questions
● Related work
● Data
● Proposed method
● Evaluation and results
● Conclusion
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3. Introduction (Information as a network)
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4. Wikipedia article recommendation
Source: https://www.wikipedia.org/
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Source: https://www.flickr.com/photos/sjcockell/8425835703

5. Motivation
Source: https://en.wikipedia.org/wiki/Programming_language_theory#See_also
● Manual search is hard
● Millions of articles
● No “See also” on each
article
● “See also” are created
manually
● Enhancement of
information search
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6. Problem formulation and research gap
● How to find and list top k articles related to the given one?(ranking
problem)
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7. Problem formulation and research gap
● How to find and list top k articles related to the given one?(ranking
problem)
● There is no such a tool currently
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8. Problem formulation and research gap
● How to find and list top k articles related to the given one?(ranking
problem)
● There is no such a tool currently
● Tools that enhance the experience with Wikipedia use neither
topological information of the Wiki graph nor history of human
navigation on it
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9. Research Questions
● Does topology reflect the relatedness of articles?
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10. Research Questions
● Does topology reflect the relatedness of articles?
● Is this information sufficient? If not, do the navigation information
enrich this information and contribute to the goodness of the
recommendation?
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11. Research Questions
● Does topology reflect the relatedness of articles?
● Is this information sufficient? If not, do the navigation information
enrich this information and contribute to the goodness of the
recommendation?
● Which of these options - solely structural information or navigational
information - is more important for good recommendation?
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12. Related work
● Navigation in information networks
● Patterns in human navigation
● Random walks
● Recommender systems
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13. Navigation in information networks
● Kleinberg: (some) networks are efficiently navigable!
● Semantic relatedness based on game paths (West et al.)
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14. Patterns in search strategies
● Move to hub, then zoom in pattern (West and Leskovec)
● Back clicking patterns (West and Leskovec)
● Article structure matters (Lamprecht et al.)
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15. Random walks
Source: https://www.wikiwand.com/en/Random_walk
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Source:
https://www.slideshare.net/pavankapanipathi/random-walk-on-graphs

16. Recommender systems
● Semantic relatedness of Wikipedia articles using personalized
PageRank (Yeh et al.)
● Random walk based recommender system for movie
recommendation (Bogers)
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17. Data
● Wikispeedia graph (condensed
version of Wikipedia):
○ Articles - 4,604
○ Links - 119,882
● Collection of players’ game paths
(on Wikispeedia graph):
○ Finished paths - 51,318
○ Unfinished paths - 24,875
● Plain text of Wikispeedia articles
~94 MiB
● Wikipedia dump (raw) ~5.7 GiB (for
obtaining ground truth
recommendations)
Source:
http://allthingsgraphed.com/2015/09/16/what-is-transhumanis
m-wikipedia/
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18. Wikispeedia graph
● Average shortest path is ~3.2
● Degree distribution
resembles power-law
● Average clustering
coefficient is 0.11
(corresponding Erdos-Renyi
graph: 0.0056)
● Global clustering coefficient
is 0.1 (corresponding
Erdos-Renyi network: 0.011)
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19. Proposed method
● Random walk agents
● Recommendation prediction
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20. Random walk agents
● Random walk agents
○ Options:
■ ,
■ ,
■ , where is number of player transitions from node i to node j
■ , where , is the number of outgoing links of node i
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21. Recommendation prediction
● Obtain ground truth recommendations for articles.
● Compute metrics from previous slide for each pair (article,
recommendation). These are positive examples.
● For each article that has ground truth recommendations, randomly
sample articles that are not recommendations for the current one
and compute metrics for them. These are negative examples.
● Assign corresponding labels, train binary classifier.
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22. Agent results, distance
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23. Agent results, visitance vs degree
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24. Agent results, similarity
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25. Evaluation
● Train/Test split and predict recommended entities of the test set
● Metrics:
○ MAP@k,
○ V@k, normalized total reciprocal rank,
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26. Classification results
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27. Metrics significance
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28. Conclusion
● Information encoded in human trails is useful in recommendation of
similar articles
● Simple structural information like degree(uniform agent) produces
second best results
● Long vs short walks -> what is better?
● Similarity is not changing with time on average and is less than
similarity between article and its true recommendations
● None of the considered transition probabilities produces very good
recommendation, however their linear combination works much
better in the context of prediction
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29. Contribution
● Designed metrics (transition probabilities) for random walk agents
● Implemented recommender system based on the agents’ walks
● Discovered that walks are local and that similarity is stable
● Proposed method to combine metrics (linear combination)
● Designed the procedure for inferring metric importances
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30. Thank you! Questions?
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31. Recommendation examples
Path based:
○ United States
○ Europe
○ United Kingdom
○ England
○ Earth
○ World War II
○ France
○ Africa
○ Germany
○ North America
Uniform:
○ United States
○ Europe
○ United Kingdom
○ France
○ Latin
○ India
○ World War II
○ Germany
○ English language
○ Spain
Pearson:
○ Harry Potter
○ Das Kapital
○ Cambodia
○ Sistine Chapel ceiling
○ Las Vegas, Nevada
○ Palm oil
○ Sigmund Freud
○ Battle of Marathon
○ Hinduism
○ Afghanistan
Tf-idf (alpha = 2):
○ Europe
○ England
○ Judaism
○ Italy
○ Turkey
○ Spain
○ Bermuda
○ Malta
○ Geography
○ Tungsten
Query: Monarchy, top 10 most occuring recommendations
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