Brief
yenta is a professional networking app launched by Japanese startup Atrae that uses artificial intelligence (AI) to optimize profile matching for its users. In hosting this competition, Atrae strives to improve the AI it uses on its platform to enable yenta users to make new, valuable connections and expand their networks.
The goal of this competition is to optimize yentaās matching algorithm by predicting the compatibility of two app users. This ensures that the app recommends the most relevant profiles to each user, and winning algorithms submitted for this competition may be used to optimize yentaās profile recommendation algorithm. With your support, we aim to help people around the world make beneficial connections that last.
ABOUT YENTA
The goal of this contest is to improve the matching algorithm on Atraeās professional networking app called yenta. Go to https://yenta.page.link/install_fbg to download and learn more about the yenta app.
*The yenta app is only available in Japan and India.
yenta is a matching app for building professional connections. Once you create a profile on the app, its native AI algorithm searches other users' profiles to find who you might be interested in and like to meet with. You swipe right on profiles you're interested in, and if the other person is also interested and swipes right on your profile, you can message, meet up, and submit a review for each other.
We encourage you to download the app to expand your network, get insider tips on new job opportunities, and gain insights that could give you an edge in this competition!
Prizes
1st Prize ($ 5000)
2nd Prize ($ 3000)
3rd Prize ($ 1000)
4th Prize ($ 600)
5th Prize ($ 400)
Timeline
- 15 Aug 2021 Competition Starts
- 30 Sep 2021 Competition Ends & Private Leaderboard Released
- 05 Oct 2021 Final Source Code Submission Deadline
- 15 Oct 2021 Winners Announced (Subject to change based on submission results)
Data Breakdown
The goal of this competition is to predict the level of compatibility of two given users to improve the profile recommendation algorithm for yenta (link to download: https://yenta.page.link/install_fbg). For this purpose, we classify the level of compatibility between user A and user B into 4 categories:
⢠No Match = 0: At least one of either user A or user B swiped left on the other, meaning there is no possibility of a match.
⢠Match = 1: Both user A and user B swiped right on each other and matched.
⢠Matched and met but unfavorable review = 2: Both user A and user B swiped right on each other and matched, then met. After the meeting, user A gave user B a review of 1-3 out of 5 (an āunfavorableā review).
⢠Matched and met and favorable review = 3: Both user A and user B swiped right on each other and matched, then met. After the meeting, user A gave user B a review of 4-5 out of 5 (a āfavorableā review).
To build this model, we provide 2 different types of data subsets: user data and interaction data.
Note that all of the data is anonymized through the use of alias IDs and multi-step vectorization models to ensure that user privacy is protected. IDs with low frequency are grouped into a category labelled āotherā with an ID of 999999.
I. User data: These files are connected through the user_id column (e.g. 41245)
ć» user_ages.csv:
# user_id: user ID
# age: user age (in years)
ć» user_educations.csv:
# user_id: user ID
# school_id: school ID
# degree_id: degree ID (just for some users)
ć» user_works.csv:
# user_id: user ID
# company_id: company ID
# industry_id: company's industry ID
(please note: one company can have multiple values; also, this column is user-selected, so values are not necessarily tied to company ID, which means that the same company ID can have different values for different users)
# over_1000_employees: variable indicating if the company has over 1000 employees or not
(please note: this column is user-selected, so values are not tied to company ID, which means the same company ID can have different values for different users)
ć» user_skills.csv:
# user_id: user ID
# skilld_id: skill ID
ć» user_strengths.csv:
# user_id: user ID
# strength_id_x: the number of votes that the user has received as review from other users
ć» user_purposes.csv:
# user_id: user ID
# purpose_id_x: whether the user marked "x" as a reason for using the app or not
ć» user_self_intro_vectors_300dims.csv:
# user_id: user ID
# num_char: number of characters in the user's self-introduction text
# num_url: number of URLs in the user's self-introduction text
# num_emoji: number of emojis used in the user's self-introduction text
# self_intro_x: value for dimension "x" of the user's vectorized self-introduction text (out of 300 dimensions)
ć» user_sessions.csv:
# user_id: user ID
# timestamp: session timestamp
II. Interaction data: These files are indexed by from-to user_id pairs (e.g. 12345-52462)
ć» interaction_review_comments_300dims.csv:
# from-to: user ID of reviewer-user ID of reviewed
# review_comment_x: value for dimension "x" of the review comment (out of 300 dimensions)
ć» interaction_swipes.csv:
# from-to: user ID of swiper-user ID of target
# timestamp: timestamp of the swipe event
# swipe_status: result of the swipe (-1 = not interested, 1 = interested)
ć» interaction_review_strengths.csv:
# from-to: user ID of reviewer-user ID of reviewed
# strength_id: ID of the strength evaluated by the reviewer
III. Train and test files: In order to train the model, we provide a train.csv file with pairs of user IDs and their corresponding scores
ć» train.csv:
# from-to: user ID of scorer-user ID of target
# score: compatibility score ID (0-3)
ć» test.csv:
# from-to: user ID of scorer-user ID of target (to be predicted)
The solution file to be provided should follow this format: the from-to IDs should be the same IDs contained in the test.csv file, and they must be in the same order.
IMPORTANT NOTE: Score values on the submission file should be formatted with at least one decimal (e.g. 0.0 instead of 0, 1.0 instead of 1) or the system will not be able to score it properly.
⢠Submission.csv
from-to, score
6280229-6293525, 1.0
670384-50085, 2.0
2271906-4685859, 1.0
...
NOTE: The maximum number of submissions that can be made per day are 3 submission.csv files.
A few minutes after submitting your solution, you will be able to see the accuracy of your solution on the submission page over a subset of the test data.
Final competition results will be based on the Private Leaderboard results, and the winner will be the user at the top of the Private Leaderboard.
FAQs
Who do I contact if I need help regarding a competition?
If you have any inquiries, please contact us at [email protected]
How will I know if Iāve won?
We will send an email to the top five winners of the competition to inform you of your ranking and begin processes for delivering your prize.
How can I report a bug?
If you discover a bug, please send an email to [email protected] with a description and details about the bug. If possible, please include a screenshot of the bug as an attachment to the email.
If I win, how can I receive my reward?
Prizes can be delivered via PayPal, wire transfer, or other suitable methods. We understand that everyone prefers different payment methods, and we endeavor to accommodate your needs as best as possible depending on your location and our ability to do so.
Why is my score 0.00037?
The predictions on your submission.csv file should be formatted with at least one decimal (e.g. 0.0 instead of 0, 1.0 instead of 1) as stated in the Guidelines, otherwise the accuracy will not be correctly displayed. We apologize for this inconvenience, and we appreciate your understanding as we work on supporting competition solutions following different formats.
Rules
1. This competition is governed by the following Terms of Participation. Participants must agree to and comply with these Terms to participate.
2. The maximum limit of submissions per day is 3. If users want to submit new files, they will have to wait until the following day to do it. Please keep this in mind when uploading a submission.csv file.
3. A competition prize will be awarded after we have received, successfully executed, and confirmed the validity of both the code and the solution. Once winners are announced and our team reaches out to them, the winners must provide the following by November 6, 2020 in order to avoid disqualification.
a. All source files required to preprocess the data
b. All source files required to build, train and make predictions with the model using the processed data
c. A requirements.txt (or equivalent) file indicating all the required libraries and their versions as needed
d. A ReadMe file containing the following:
⢠Clear and unambiguous instructions on how to reproduce the predictions from start to finish including data pre-processing, feature extraction, model training and predictions generation
⢠Environment details regarding where the model was developed and trained, including OS, memory (RAM), disk space, CPU/GPU used, and any required environment configurations required to execute the code
⢠Clear answers of the following questions:
- Which data files are being used?
- How are these files processed?
- What is the algorithm used and what are its main hyperparameters?
- Any other comments considered relevant to understanding and using the model
In the event these items are not provided or do not meet the minimum requirements listed above, we will not be able to award the winner with their respective prize.
4. If two or more participants have the same score on the leaderboard, the participant who submitted the winning file first will be considered the winner.
5. The dataset used for this competition is derived from real-world data that has been anonymized, so please do not use any models developed utilizing this data on similar matching services.
6. If you have any inquiries about this competition, please donāt hesitate to reach out to us at [email protected]. We ask that users do not contact Atrae directly.
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My Submissions
Non-Disclosure Agreement (NDA)
An agreement to not reveal the information shared regarding this competition to others.
These Terms of Participation (āAgreementā) are hereby entered into on the date of your participation
conditional upon your agreement to these terms (āEffective Dateā) between you (āParticipantā), as a
participant in the #SwipeToSuccess competition (the āCompetitionā) hosted at bitgrit.net (the āCompetition
Siteā), and bitgrit Inc. (āBitgritā).
IMPORTANT, READ CAREFULLY: Your participation in the Competition on the above Competition Site is
conditional upon your comprehension of, compliance with, and acceptance of these terms. Please review
thoroughly before accepting.[MOU1]
I Clauses of Non-Disclosure
1. Confidential Information
(1) Confidential Information shall mean any and all information disclosed by
Bitgrit to the Participant with regard to the entry and participation in
the Competition, including (iļ¼ metadata, source code, object code,
firmware etc. and, in addition to these, (ii) analytes, compilations or any
other deliverable produced by the Participant in which such disclosed
information is utilized or reflected.
(2) Confidential Information shall not include information which;
(a) is now or hereafter becomes, through no act or omission on the Participant, generally known or
available to the public, or, in the present or into the future, enters the public domain through no act
or omission by the Participant;
(b) is acquired by the Participant before receiving such information from Bitgrit and such acquisition
was without restriction as to the use or disclosure of the same;
(c) is hereafter rightfully furnished to the participant by a third party, without restriction as to use or
disclosure of the same.
2. Non-Disclosure Obligation
The Participant agrees:
(a) to hold Confidential Information in strict confidence;
(b) to exercise at least the same care in protecting Confidential Information from disclosure as the
party uses with regard to its own confidential information;
(c) not use any Confidential Information except for as it concerns the Purpose elaborated upon above;
(d) not disclose such Confidential Information to third parties;
(e) to inform Bitgrit if it becomes aware of an unauthorized disclosure of Confidential Information.
3. No Warranty
All Confidential Information is provided āas is.ā None of the Confidential Information shall contain any
representation, warranty, assurance, or integrity by Bitgrit to the Participant of any kind.
4. No Assignment of Rights
The Participant agrees that nothing contained in this Agreement shall be construed as conferring,
transferring or granting any rights to the Participant, by license or otherwise, to use any of the Confidential
Information.
5. Return/Destruction of Confidential Information
(1) On the request of Bitgrit, the Participant shall promptly, in a manner specified by Bitgrit, return or
destroy the Confidential Information along with any copies of said information.
(2) Bitgrit may request the Participant to submit documentation to confirm the destruction of said
Confidential Information to Bitgrit in the event that Bitgrit requests the Participant to destroy this
Confidential Information, pursuant to the provision of the preceding paragraph.
II Rights to Deliverables
1. Retainment of Rights
Copyrights (including rights stipulated in Clause 27 and Clause 28 of the Copyright Act), patents,
rights to patent, rights to design, utility model rights, and other intellectual property rights (herein
referred to collectively as āIntellectual Property Rightsā) in relation to deliverables prepared by
the Participant accompanying the Participantās entry and participation in the Competition,
including algorithms, programs, source codes, and other items (collectively, āSubmitted
Algorithmsā) shall belong to the participant.
2. Restrictions on Use
The Participant hereby agrees to not utilize Submitted Algorithms to or for businesses, business
endeavors, products, or services in competition with Bitgrit or with the Competition co-host.
3. Authorization of Non-compensatory Use
The Participant hereby authorizes and consents to Bitgrit and/or relevant third parties utilizing,
analyzing, altering, or further reauthorizing the use of the Submitted Algorithm(s) to other third
parties and will not make claims or demands for monetary compensation in regard to the above
purposes.
4. Representations and Warranties
The Participant hereby declares and warrants that the Participantās, Bitgritās, and the related third
partyās use of the Submitted Algorithms does not violate or infringe upon the intellectual property
rights, business secrets, or other rights of any other third party.
5. Warranty Against Exercising of Moral Rights
The Participant agrees to not exercise moral rights to Bitgrit or to related third parties in regard to
the Submitted Algorithms.
6. Rights Regarding Modified and Derivative Works
The Participant hereby agrees that Intellectual Property Rights and other rights regarding any
modified or derivative works created from the Submitted Algorithms shall belong to the creator
of that modified or derivative work.
III General Clauses
1. No Assignment
The Participant shall not assign, transfer or otherwise dispose of this Agreement or any of its rights,
interest or obligations hereunder without the prior written consent of Bitgrit.
2. Injunctive Relief
In the event of a breach or the possibility of breach of this Agreement by the Participant, in addition
to any remedies otherwise available, Bitgrit shall be entitled to seek injunctive relief or equitable
relief, as well as monetary damages.
3. Governing Law
This Agreement shall be governed by and construed and interpreted under the laws of Japan without
reference to its principles governing conflicts of laws.
4. Jurisdiction
The parties hereto submit to the exclusive jurisdiction of the courts of Japan, where the head office of
Bitgrit, is located, in respect to all conflicts or controversies arising from or in relation to this
competition.
Uploading a new submission file will overwrite the existing file.
Terms & Conditions
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