Weakly hierarchical lasso based learning to rank in best answer prediction

In community question and answering sites, pairs of questions and their high-quality answers (like best answers selected by askers) can be valuable knowledge available to others. However lots of questions receive multiple answers but askers do not label either one as the accepted or best one even when some replies answer their questions. To solve this problem, high-quality answer prediction or best answer prediction has been one of important topics in social media. These user-generated answers often consist of multiple 'views', each capturing different (albeit related) information (e.g., expertise of the asker, length of the answer, etc.). Such views interact with each other in complex manners that should carry a lot of information for distinguishing a potential best answer from others. Little existing work has exploited such interactions for better prediction. To explicitly model these information, we propose a new learning-to-rank method, ranking support vector machine (RankSVM) with weakly hierarchical lasso in this paper. The evaluation of the approach was done using data from Stack Overflow. Experimental results demonstrate that the proposed approach has superior performance compared with approaches in state-of-the-art.