SBIR Phase I: Advertising Sales and Traffic Optimization: Difficult Customer-Requested Optimization Constraints and Scalability on Real Data

SBIR 第一阶段：广告销售和流量优化：客户要求的困难的优化约束和真实数据的可扩展性

• 批准号: 1345567
• 负责人: John Dickerson
• 金额: $ 15万
• 依托单位: Optimized Markets, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-01-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1345567&HistoricalAwards=false
• 关键词: SBIR; Phase; Advertising; Sales; Traffic

This SBIR Phase I project proposes to develop a new optimization engine that supports important allocation constraints requested by potential customers, and make it scalable using novel techniques and real customer data. Determining who buys what items from whom under rich expressiveness is a provably hard optimization problem that requires sophisticated algorithms. The PI?' academic group has developed technology for novel tree search / integer programming as well as automated problem (re)formulation, and they have adapted it to address the advertising market's needs. Proposed work includes developing new optimization techniques.The broader/commercial impact involves a significant efficiency improvement - i.e., improvement in the allocation that benefits both buyers and sellers - in a huge industry. The US TV ad market is $75 billion annually. Experiences from deploying approaches similar to that proposed here into other markets suggest 12% - 41% efficiency improvements. The efficiency gains will significantly enhance US competitiveness. The structured, optimized market begets a fairer playing field and broader access - also for new entrants. Consumers also benefit because they will see more relevant ads rather than having their time and attention taken by irrelevant ones. The on-ramp to the advertising market is a single-seller, multi-buyer setting. Later the footprint can be generalized to expressive exchanges. The approach and technology also apply to other markets such as those for electricity and pollution rights. The proposed optimization technology applies to applications beyond markets also. The company has a plan for disseminating the results broadly via academic outlets, courses, industrial contacts, and government contacts. They will also plan to engage minorities and undergraduates. The effort describes a symbiotic relationship between an innovation ecosystem and the proposed project.

该 SBIR 第一阶段项目建议开发一种新的优化引擎，支持潜在客户要求的重要分配约束，并利用新技术和真实客户数据使其可扩展。在丰富的表现力下确定谁从谁那里购买什么商品是一个经过证明的困难优化问题，需要复杂的算法。 PI？学术小组开发了新颖的树搜索/整数规划以及自动问题（重新）表述的技术，并对其进行了调整以满足广告市场的需求。拟议的工作包括开发新的优化技术。更广泛/商业的影响涉及在一个巨大的行业中显着的效率改进，即对买家和卖家都有利的分配改进。美国电视广告市场每年价值 750 亿美元。将与此处提出的类似方法部署到其他市场的经验表明，效率可提高 12% - 41%。效率的提高将显着增强美国的竞争力。结构化、优化的市场带来了更公平的竞争环境和更广泛的准入——对于新进入者来说也是如此。消费者也会受益，因为他们会看到更多相关的广告，而不是让时间和注意力被不相关的广告占据。广告市场的入口是单一卖家、多买家的环境。后来足迹可以推广到表达性交流。该方法和技术也适用于其他市场，例如电力和污染权市场。所提出的优化技术也适用于市场以外的应用。该公司计划通过学术渠道、课程、行业联系和政府联系广泛传播研究结果。他们还将计划让少数族裔和本科生参与进来。这项工作描述了创新生态系统和拟议项目之间的共生关系。