1. RecSys Challenge 2016
http://recsyschallenge.com - @recsyschallenge
Martha, Róbert, András, Daniel, Fabian
RecSys, Boston, September 2016

2. Agenda
Proceedings: titanpad.com/recsyschallenge2016
• 09:00-10:30 Welcome + Short presentations
• 10:30-11:00 Coffee break
• 11:00-12:30 Full Papers
• 12:30-14:00 Lunch break
• 14:00-15:30 Full Papers / Top 3
• 15:30-16:00 Coffee break
• 16:00-17:30 Panel Discussion: RecSys Challenge ‘17
2

3. Job recommendations

4. Job recommendations

5. Example: item (job posting)
5

6. RecSys Challenge
Given a user, the goal is to predict those job postings that the
user will interact with.
6
?
Scala Dev,
Hamburg
job postings
Scala
Engineer
2 months of impressions &
interactions
click
bookmark

7. Datasets
1. Training data:
• User demographics (jobtitle, discipline, industry, career level, # CV entries,
country, region) [1M]
• Job postings (title, discipline, industry, career level, country region) [1M]
• Interactions (user_id, item_id, interaction_type, timestamp) [10M, 2 months]
• Impressions (user_id, item_id, week) [30M, 2 months]
2. Task files:
• Users (= User IDs for whom recommendations should be computed) [150k]
• Candidate items (= item IDs that are allowed to be recommended) [300k]
3. Solution (secret)
• Interactions (user_id, item_id) [1M, 1 week]
Anonymization (Strings  IDs; users and interactions are enriched with
artitificial noise) 7

8. Interaction Data
includes interactions that were not performed on recommendations
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1"
10"
100"
1000"
10000"
100000"
1000000"
1" 10" 100" 1000" 10000" 100000"
number'of'users/items'that'performed/
received'X'interac5ons'
number'of'interac5ons'
items"(train)"
users"(train)"
items"(test)"
users"(test)"
81%$
5%$
2%$
12%$
interac( on*types*
clicks$
replies$
bookmarks$
deletes$

9. Evaluation Measure
Mixture of…
- Precision@k (k = 2, 4, 6, 20)
= fraction of relevant items in the top k
- Recall@30 = fraction of relevant
items in the top k
- Success@30 = probability that at
least one relevant item was
recommended in the top 30
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10. Who participated?
• 119 teams participated (366 teams registered)
• Countries:
 USA (25%)
 Germany (11%)
 China (9%)
 France (7%)
 Hungary (4%)
• Type of organization:
 academia (∼25%)
 industry (∼75%)
 most common industry: Internet & IT
 larger companies such as Yandex, Alibaba, Microsoft or
Amazon as well as start-ups
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11. Top score over time
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Number'of'submissions'during'week'X'
Top'score'at'the'end'of'week'X'
Week'
top"score"(full)"
#submissions"

12. Number of submissions per team
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number'of'submissions'
rank'of'team'

13. Overlap with XING’s recommender
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number'of'users'
number'of'overlapping'recommenda3ons'

14. Outlook for 2017
• Current plan:
 Domain: again job recommendations
 Additional perspectives:
 is the user a good candidate for the job?
 Novelty (recommending new jobs)
 New users (recommending jobs to new users)
 Additional features (e.g. clicks from recruiters on profiles)
 Additional tooling:
 Proper API for submitting solutions
 Advanced Baseline implementations (building up on this year’s solutions)
• Goal: offline + online (!!) evaluation
• More details: panel discussion in the afternoon
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15. Thank you to PC!
• Alejandro Bellogín, Universidad Autónoma de Madrid, Spain
• Paolo Cremonesi, Politecnico di Milano, Italy
• Simon Dooms, Trackuity, Belgium
• Balasz Hidasi, Gravity R&D, Hungary
• Levente Kocsis, Hungarian Academy of Sciences, Hungary
• Andreas Lommatzsch, TU Berlin, Germany
• Katja Niemann, XING AG, Germany
• Alan Said, University of Skövde, Sweden
• Yue Shi, Yahoo Labs, USA
• Marko Tkalcic, Free University of Bozen-Bolzano, Italy
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16. 16
Thank you to
RecSys Challenge
participants!

17. Agenda
• 09:00-10:30 Welcome + Short presentations
• 10:30-11:00 Coffee break
• 11:00-12:30 Full Papers
• 12:30-14:00 Lunch break
• 14:00-15:30 Full Papers / Top 3
• 15:30-16:00 Coffee break
• 16:00-17:30 Panel Discussion: RecSys Challenge ‘17
17

18. Thank you
@recsyschallenge
http://recsyschallenge.com
www.xing.com