Research

We are seeking solutions that address the following topics within advertising: analysis of experiments; addressing non-response bias and missing data; complex statistical and machine learning models; causal inference with observational data; record linkage and matching individuals across different channels. Updates to inferences may range from daily to hourly, and may be conducted with a small number of active data-controlling parties or necessitate a distributed approach with billions of individual browsers or devices.

We are particularly interested in applications using one or more of the following privacy-enhancing technologies in their research proposal (we are also open to applications leveraging technologies not listed below):

• Differential privacy
• Secure multiparty computation
• Homomorphic encryption
• Federated learning
• Zero-knowledge proofs
• Secure aggregation
• Trusted execution environments

Below, we describe some specific examples of topics that are of interest to Facebook, but we will gladly review proposals for areas that we have not yet considered:

• A/B experimentation: Questions about effectiveness of ads are best answered by comparing the overall behavior of two groups of users — for example, one group that is exposed to the ads and one group that is not. Computing the difference in purchases or sales across these groups helps advertisers understand the true impact of advertising. Facilitating such user-level testing necessitates identification of users across browsers and devices and also across publishers (which serve the ads) and advertisers (which record sales or purchases). The result, “Lift in sales caused by ads,” is, however, an aggregate measure and is not necessarily private information. The goal is to enable Lift measurement that leverages person-level ad exposure and sales data, while also preserving the privacy of consumer and business data involved.
• Cross-media reach and frequency measurement: Reaching as many people as possible in their target audience is a great way for advertisers to improve awareness of their brand and make sure their brand/product is top of mind for consumers. At the same time, in the interest of increasing efficiency of their media spend, they don’t want to show too many ads to the same person across devices, browsers, apps, and websites. This requires measuring the reach of their advertising campaigns in terms of unique individuals exposed to any ad (across desktop browsers, mobile browsers, mobile apps), as well as the frequency profile of multiple ad exposures to the same individual. Traditional methods have relied on vendors accumulating individual-level advertising exposure data across media channels and then computing reach and frequency. The goal is to enable cross-media measurement that leverages person-level ad exposure data across media while also preserving the privacy of consumers who viewed or interacted with the ad.
• Record linkage: Most advertising optimization and measurement systems are designed to understand the consumers’ immediate purchase actions after interacting with an ad to offer better ad experience for them. Legacy techniques involve facilitating record linkage across businesses (for example, to enable businesses to communicate consumers’ purchase decisions to ad publishers) by sharing encrypted data sets with each other in a secure way, and the record linkage process takes place in one of the parties’ servers. In such a system, privacy guarantees are often contractual. The goal is to use advancements in privacy technology to enable record linkage with built-in technical privacy guarantees.
• Modeled attribution: Advertisers spend their marketing budget across multiple media channels, and consumers can interact with many of these advertisements before they make a purchase on the advertiser’s site. To measure such media investments, advertisers typically use modeled attribution solutions (like multitouch attribution) that incorporate the various ad exposures and purchase behavior in a statistical model to determine relative contribution of certain types of ads toward the likelihood of purchase. The goal is to enable such measurement using privacy-preserving technologies.
• Optimization towards specific objectives: Advertisers tend to pick a measurable objective that they would like to maximize when they buy ads on a specific publisher. As an example, “website conversion” is an objective where advertisers want as many of a particular valuable event (like “add to cart” or “subscribe to newsletter”) to happen as possible. To maximize such objectives for advertisers, publishers train ML models that look for correlation between onsite features (like age or gender a person may have entered into their Facebook profile, or historical click-through rate of that particular ad) and offsite labels (whether or not “website conversion” happened); predictions from these models are then used in delivering subsequent ads to maximize advertiser value. The goal is to enable optimization model training with individual-level features and labels while also preserving privacy of offsite consumer data.

We will prioritize proposals that are pragmatic, use-inspired and grounded in empirical applications. We will pay special attention to proposals that include plans for producing useful, easy-to-use, open source software as part of the research output.

Proposals should include

• Summary of the project (1–2 pages). Provide a clear explanation of the area of focus, a description of techniques, any relevant prior work, and a timeline with milestones and expected outcomes. Please also include a concise statement of the scientific contribution and routes to eventual deployment. References are not included in the two-page limit, but please keep these concise.
• Curriculum vitae(s). Provide the name of each researcher involved in the proposed work with their CV/résumé or link to Google Scholar page.
• A one-paragraph biography of each principal researcher
• Organization details, including tax information and administrative finance contact details
• Keywords. Please select from the following keywords: advertising algorithms, ad targeting, ad personalization, ad optimization, cross-channel, cross-publisher, audience measurement, sales measurement, cross-publisher, advertising effectiveness, RTB, programmatic ads, ads reporting, experimentation, record linkage.

Eligibility

• This RFP is open to current full-time faculty at accredited academic institutions that award research degrees to PhD students. This includes individual researchers addressing a well-defined problem as well as multiple university departments, with a diverse set of skills, collaborating to solve a more complex multidisciplinary challenge.
• Organizations must be an accredited academic institution (PhD-granting) with recognized legal status in their respective country (equal to 501(c)(3) status under the United States Internal Revenue Code).
• Applicants must be the Principal Investigator on any resulting award. Funding will be disbursed to the institution of the Principal Investigator of any resulting award.
• Awards must comply with applicable U.S. and international laws, regulations, and policies.
• Facebook will not provide access to any Facebook, Instagram, or WhatsApp data for awarded projects. Any data collected by research teams must comply with Facebook’s terms and policies and have approval from the university’s institutional review board, if applicable. Please be aware that Facebook does not allow the automated scraping of public information from the platform.

Budget and payment

• Award amount can vary but is typically up to $60K.
• Payment will be made to the principal researcher’s host university as an unrestricted gift. Overhead is limited to 5 percent for gifts. Overhead amounts should be built into proposed budgets.

Facebook’s decisions will be final in all matters relating to this solicitation, including interpretation of these Terms and awards at any time. All items apply unless otherwise stated in each RFP. Please Read These Terms Carefully Before Proceeding.

• By submitting this proposal, you are authorizing Facebook to evaluate the proposal for a potential award, and you agree to the terms herein.
• You agree that Facebook will not be required to treat any part of the proposal as confidential or protected by copyright, and may use, edit, modify, copy, reproduce and distribute all or a portion of your proposal in any manner for the sole purposes of administering the website and evaluating the contents of the proposal.
• You agree and acknowledge that personal data submitted with the proposal, including name, mailing address, phone number, and email address of you and other named researchers in the proposal may be collected, processed, stored and otherwise used by Facebook for the purposes of administering the website and evaluating the contents of the proposal.
• You acknowledge that neither party is obligated to enter into any business transaction as a result of the proposal submission, Facebook is under no obligation to review or consider the proposal, and neither party acquires any intellectual property rights as a result of submitting the proposal.
• Any feedback you provide to Facebook in the proposal regarding its products or services will not be treated as confidential or protected by copyright, and Facebook is free to use such feedback on an unrestricted basis with no compensation to you.
• Applicants represent and warrant that they have authority to submit a proposal in connection with this RFP and grant the rights set forth herein on behalf of their organization.

EVENT ATTENDANCE

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License

By submitting an RFP, except where prohibited by law, each applicant grants to the Released Parties (defined below) (and their agents, successors, and assigns) the irrevocable, transferable, sublicensable, absolute right and permission to use, edit, modify, copy, reproduce, and distribute applicants’ RFP proposal (with or without reference to each person associated with the RFP) in perpetuity on the internet in any language, throughout the world, for the limited purposes of administering and promoting the RFP, Facebook’s Research program, and publicizing the gift recipients.

General Rules

Facebook’s decisions will be final in all matters relating to this RFP, including interpretation of these Terms and award of gifts at any time. Facebook shall have no liability for organizations’ use of any gifts provided hereunder, including any use of gifts not in connection with the RFP.

All grant funds or other benefits provided by Facebook in connection with this RFP shall be used only in accordance with applicable laws and shall not be used in any way, directly or indirectly, to facilitate any act that would constitute bribery or an illegal kickback, an illegal campaign contribution, or would otherwise violate any applicable anti-corruption or political activities law. Gifts may not be transferred (a) to any individual, entity, or country prohibited by any applicable U.S. or non-U.S. export controls and trade sanctions; (b) to anyone on U.S. or non-U.S. government restricted parties lists; or (c) for any purpose prohibited by applicable export controls and trade sanctions, including nuclear, chemical or biological weapons, or missile technology applications without the required government authorizations. You acknowledge that Facebook is subject to U.S. economic restrictions and trade sanctions. As such, Facebook reserves the right to deny distribution of any gift when required by applicable law. The applicants agree to comply with all applicable laws and regulations during throughout the RFP and while using any funds that may be received by Facebook.

Applicants agree not to accept an award made by Facebook if (a) it would interfere with their official duties, (b) it does not comply with applicable regulations, policies, and rules of applicant’s institution, or applicable laws, regulations, rules, judgments, and orders of any court or governmental authority; or (c) it conflicts with any other obligation the applicant or applicant’s institution have to any other party. Applicant also shall not perform any official action to improperly benefit Facebook.

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LIMITATION OF LIABILITY

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Disclaimers

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Disputes/Governing Law

Except where prohibited by law, any and all disputes, claims, and causes of action between an applicant and any Released Party arising out of or connected with this RFP or these Terms must be resolved individually, without resort to any form of class action or legal action on behalf of any group. Further, in any such dispute, under no circumstances will an applicant be permitted or entitled to obtain awards for, and hereby waives all rights to claim punitive, incidental or consequential damages, or any other damages, including attorneys’ fees, other than the applicant’s actual out-of-pocket expenses (if any), not to exceed ten dollars ($10) and each applicant further waives all rights to have damages multiplied or increased.

Except where prohibited by law, this RFP and any dispute arising under these Terms or related to these Terms or the RFP (whether for breach of contract, tortious conduct, or otherwise) will be governed by the internal laws of the State of California, U.S.A., without giving effect to its conflicts of law or choice of law principles or rules that would cause the application of the laws of any other jurisdiction. Except where prohibited by law, any legal actions, suits or proceedings related to this RFP (whether for breach of contract, tortious conduct, or otherwise) will be brought exclusively in the state or federal courts located in or having jurisdiction over San Mateo County, California, U.S.A., and each applicant accepts and submits to the personal jurisdiction of those courts with respect to any legal actions, suits or proceedings arising out of or related to this RFP.