A novel machine learning approach to rank web forum posts

Xiaohui Han, Jun Ma, Yun Wu, Chaoran Cui

Abstract: Since the user generated contents in Web forums are rich but vary in quality, ranging from excellent detailed opinions to simple repetition of the content of previous, or even spams, it is difficult to find high quality information in the process of post browsing, retrieval and other Web forum applications. In this paper, we propose a novel machine learning approach named LGPRank to evaluate the web forum posts, where a genetic programming architecture is used to rank Web forum posts according to the qualities of their contents. In order to address the shortcomings of current studies, we take both the semantic-free and semantic-specific information of a post into account. We propose a set of new features named Latent Dirichlet Allocation (LDA) semantic features which are computed in LDA topic space. The proposed features as well as content surface features and forum specific features are used in the learning process. Experiments are conducted on three web forum datasets in comparison with methods used in prior ranking research. LGPRank outperforms all the other methods in terms of P@N, NDCG@N and MAP measures. Furthermore, the experimental results also indicate that the proposed LDA semantic features have a positive effect in improving the ranking performance.