RecSys Challenge 2016http://recsyschallenge.com - @recsyschallengeMartha, Róbert, András, Daniel, FabianRecSys, Boston, September 2016

AgendaProceedings: 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

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Job recommendations

Job recommendations

Example: item (job posting)5

RecSys ChallengeGiven a user, the goal is to predict those job postings that the user will interact with.

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Scala Dev (m/w)

?Scala Dev, Hamburg

job postings

Scala Engineer

2 months of impressions & interactions

click

bookmark

Datasets1. 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)

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Interaction Data includes interactions that were not performed on recommendations

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Evaluation MeasureMixture 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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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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Top score over time

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Number of submissions per team

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Overlap with XING’s recommender

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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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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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Thank you to RecSys

Challenge participants!

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

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Thank you @recsyschallenge

http://recsyschallenge.com

www.xing.com