CAREER: Learning- and Incentives-Based Techniques for Aggregating Community-Generated Data

University Of California-Los Angeles, Los Angeles CA

Investigators

Abstract

The Internet has led to the availability of novel sources of data on the preferences, behaviors, and beliefs of massive communities of users. Both researchers and engineers are eager to aggregate and interpret this data. However, websites sometimes fail to incentivize high-quality contributions, leading to variable quality data. Furthermore, assumptions made by traditional theories of learning break down in these settings. This project seeks to create foundational machine learning models and algorithms to address and explain the issues that arise when aggregating local beliefs across large communities, and to advance the state-of-the-art understanding of how to motivate high quality contributions. The research can be split into three directions: 1. Developing mathematical foundations and algorithms for learning from community-labeled data. This direction involves developing learning models for data from disparate (potentially self-interested or malicious) sources and using insight from these models to design efficient learning algorithms. 2. Understanding and designing better incentives for crowdsourcing. This direction involves modeling crowdsourcing contributions to determine which features to include in systems to encourage the highest quality contributions. 3. Introducing novel economically-motivated mechanisms for opinion aggregation. This involves formalizing the properties a prediction market should satisfy and making use of ideas from machine learning and optimization to derive tractable market mechanisms satisfying these properties. This research will have clear impact on industry, especially for web-based crowdsourcing. The PI will pursue her long-term goal of attracting and retaining women in computer science via her involvement in workshops and mentoring programs. Results will be disseminated at http://www.cs.ucla.edu/~jenn/projects/CAREER.html.