Published: 2011 Oktober
Buchtitel: Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (KDIR)
Erscheinungsort: Paris, France
We tackle the problem of expert retrieval in Social Question Answering (SQA) sites. In particular, we consider the task of, given an information need in the form of a question posted in a SQA site, ranking potential experts according to the likelihood that they can answer the question. We propose a discriminative model (DM) that allows to combine different sources of evidence in a single retrieval model using machine learning techniques. The features used as input for the discriminative model comprise features derived from language models, standard probabilistic retrieval functions and features quantifying the popularity of an expert in the category of the question. As input for the DM, we propose a novel feature design that allows to exploit language models as features. We perform experiments and evaluate our approach on a dataset extracted from Yahoo! Answers, recently used as benchmark in the CriES Workshop, and show that our proposed approach outperforms i) standard probabilistic retrieval models, ii) a state-of-the-art expert retrieval approach based on language models as well as iii) an established learning to rank model.