Browsing by Author "Li, Tianyi"
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- Collaborative Filtering for IDEALLi, Tianyi; Nakate, Pranav; Song, Ziqian (2016-05-04)The students of CS5604 (Information Retrieval and Storage), have been building an Information Retrieval System based on tweet and webpage collections of the Digital Library Research Laboratory (DLRL). The students have been grouped into smaller teams such as Front End team, Solr team, and Collaborative Filtering team, which are building the individual subsystems of the entire project. The teams are collaborating among themselves to integrate their individual subsystems. The Collaborative Filtering (CF) team has been building a recommendation system that can recommend tweets and webpages to users based on content similarity of document pairs as well as user pair similarity. We have finished building the recommendation system so that when the user starts using the system they will be recommended to documents that are similar to those returned by their queries. As more users coming in, they will be also referred to documents that similar users were interested in.
- Solving Mysteries with Crowds: Supporting Crowdsourced Sensemaking with a Modularized Pipeline and Context SlicesLi, Tianyi (Virginia Tech, 2020-07-28)The increasing volume and complexity of text data are challenging the cognitive capabilities of expert analysts. Machine learning and crowdsourcing present new opportunities for large-scale sensemaking, but it remains a challenge to model the overall process so that many distributed agents can contribute to suitable components asynchronously and meaningfully. In this work, I explore how to crowdsource sensemaking for intelligence analysis. Specifically, I focus on the complex processes that include developing hypotheses and theories from a raw dataset and iteratively refining the analysis. I first developed Connect the Dots, a web application that implements the concept of "context slices" and supports novice crowds in building relationship networks for exploratory analysis. Then I developed CrowdIA, a software platform that implements the entire crowd sensemaking pipeline and the context slicing for each step, to enable unsupervised crowd sensemaking. Using the pipeline as a testbed, I probed the errors and bottlenecks in crowdsourced sensemaking,and suggested design recommendations for integrated crowdsourcing systems. Building on these insights and to support iterative crowd sensemaking, I developed the concept of "crowd auditing" in which an auditor examines a pipeline of crowd analyses and diagnoses the problems to steer future refinement. I explored the design space to support crowd auditing and developed CrowdTrace, a crowd auditing tool that enables novice auditors to effectively identify the important problems with the crowd analysis and create microtasks for crowd workers to fix the problems.The core contributions of this work include a pipeline that enables distributed crowd collaboration to holistic sensemaking processes, two novel concepts of "context slices" and "crowd auditing", web applications that support crowd sensemaking and auditing, as well as design implications for crowd sensemaking systems. The hope is that the crowd sensemaking pipeline can serve to accelerate research on sensemaking, and contribute to helping people conduct in-depth investigations of large collections of information.