User Modeling, Adaptation and Personalization

Abstract

Recommender systems face difficulty in cold-start scenarios where a new user has provided only few ratings. Improving cold-start performance is of great interest. At the same time, the growing number of adaptive systems makes it ever more likely that a new user in one system has already been a user in another system in related domains. To what extent can a user model built by one adaptive system help address a cold start problem in another system? We compare methods of cross-system user model transfer across two large real-life systems: we transfer user models built for information seeking of scientific articles in the SciNet exploratory search system, operating over tens of millions of articles, to perform cold-start recommendation of scientific talks in the CoMeT talk management system, operating over hundreds of talks. Our user study focuses on transfer of novel explicit open user models curated by the user during information seeking. Results show strong improvement in cold-start talk recommendation by transferring open user models, and also reveal why explicit open models work better in cross-domain context than traditional hidden implicit models.

Using Open APIs To Drive Financial Inclusion Via Credit Scoring Built on Telecoms Data

“…Pedro, Proserpio, and Oliver conducted a study using real credit default data from about 60,000 mobile consumers in a Latin American country and benchmarked the data from a traditional credit scoring mechanism against the data from mobile phone usage [25]. They were able to model consumers' propensity to default on credit based on their mobile phone usage.…”