Enriching cold start personalized language model using social network information

Abstract

We introduce a generalized framework to enrich the personalized language models for cold start users. The cold start problem is solved with content written by friends on social network services. Our framework consists of a mixture language model, whose mixture weights are estimated with a factor graph. The factor graph is used to incorporate prior knowledge and heuristics to identify the most appropriate weights. The intrinsic and extrinsic experiments show significant improvement on cold start users. © 2014 Association for Computational Linguistics.