Pilot Project 2.1 - Data Recommendation using Machine Learning and Crowdsourcing

Leader: Xiaoqian Jiang, Ph.D. Department of Biomedical Informatics UCSD

Collaborators: Zhaohui Qin, Ph.D., Jaideep Vaidya, Ph.D., Aditya Menon, Ph.D. (non funded), and Hwanjo Yu, Ph.D. (non funded)

Recommendation systems have witnessed a lot of successes in movie suggestion, online shopping, content searching, etc. An advanced data recommendation can promote scientific discovery and improve the healthcare quality. 

This project aims at making data recommendation based on content similarity, user background, and the context. In specific, we plan to build a hierarchical latent topic model over millions of PMC articles and propagate topic similarity to cited data to construct a graph of data. We record the pattern of query (as well as user information) and make recommendations to users based on the topological structure of the sub-graph that was visited.

Pilot Google Drive Folder