Paper: Enriching Cold Start Personalized Language Model Using Social Network Information

ACL ID P14-2100
Title Enriching Cold Start Personalized Language Model Using Social Network Information
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

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 es- timated with a factor graph. The factor graph is used to incorporate prior knowledge and heuris- tics to identify the most appropriate weights. The intrinsic and extrinsic experiments show significant improvement on cold start users.