This is a presentation of a workshop on recommendation strategies for Personal Learning Networks, held on 11/7/2013 at the PLE conference in Berlin (DE). It discusses the matching on similarity and matching of dissimilarity.
Introduce yourself to the others at your
table. Make a mental note of who you
want to meet up with later in the
Write down 10 tags - one tag per card -
• your work
• the topics you find important in the PLE
Pool all the tags in the centre of the table,
and read them all. Staple similar tags
together, and choose one marker tag for
Example: create, creativity and creation
Next, individually, make tagsets: write
down marker tags that belong together
according to you
Match on similarity with everyone at the
A = total number of used cards = 5 + 4 = 9
B = number of piles which have both your colours
Match on dissimilarity with everyone at
Ex: Overlapping tagsets, with overlap 2:
[learning, writing, reading]
[learning, network, writing, blog, wiki]
Who are your best matches? Share the
outcome with the others at the table. Do
the results match your initial ‘gut’ feeling?
What did we do?
Similarity matching or Dissimilarity
Method: User profiles
• Scoop.IT profiles as the starting point
• Selection on the basis of Scoop.IT posts
• Keyword extraction + stemming
• based on Similarity
• based on Dissimilarity
1.Content Relevance: The Scoop.IT contains
new and relevant content for me
2.Experience of Breakdown: This Scoop.IT
feed makes me re-assess my thoughts about this
3.Desire to Connect: I would like to engage in
a discussion with the curator of this Scoop.IT
1.Experience of Breakdown strongly
correlates with Desire to Connect
If you feel you have learnt something
from someone, you very likely want to
2.Matching on dissimilarity is better at
predicting Experience of Breakdown
• Questions? Comments?
• User profiles: How can qualitative
profiles be improved?
• Matching: How can qualitative
matching be improved?