A unified posterior regularized topic model with maximum margin for learning-to-rank
Refereed conference paper presented and published in conference proceedings

替代計量分析
.

其它資訊
摘要While most methods for learning-to-rank documents only consider relevance scores as features, better results can often be obtained by taking into account the latent topic structure of the document collection. Existing approaches that consider latent topics follow a two-stage approach, in which topics are discovered in an unsupervised way, as usual, and then used as features for the learning-to-rank task. In contrast, we propose a learning-to-rank framework which integrates the supervised learning of a maximum margin classifier with the discovery of a suitable probabilistic topic model. In this way, the labelled data that is available for the learning-to-rank task can be exploited to identify the most appropriate topics. To this end, we use a unified constrained optimization framework, which can dynamically compute the latent topic similarity score between the query and the document. Our experimental results show a consistent improvement over the state-of-the-art learning-to-rank models.
著者Jameel S., Lam W., Schockaert S., Bing L.
會議名稱24th ACM International Conference on Information and Knowledge Management, CIKM 2015
會議開始日19.10.2015
會議完結日23.10.2015
會議地點Melbourne
會議國家/地區澳大利亞
出版年份2015
月份10
日期17
卷號19-23-Oct-2015
頁次103 - 112
國際標準書號9781450337946
語言英式英語
關鍵詞Learning-to-rank, Maximum margin learning, Topic models

上次更新時間 2021-18-01 於 00:32