Enriching Cold Start Personalized Language Model Using Social Network Information
Yu-Yang Huang, Rui Yan, Tsung-Ting Kuo, Shou-De Lin · 2014
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.