@inproceedings{huang2013learning,
author = {Huang, Po-Sen and He, Xiaodong and Gao, Jianfeng and Deng, Li and Acero, Alex and Heck, Larry},
title = {Learning Deep Structured Semantic Models for Web Search using Clickthrough Data},
year = {2013},
month = {October},
abstract = {Latent semantic models, such as LSA, intend to map a query to its relevant documents at the semantic level where keyword-based matching often fails. In this study we strive to develop a series of new latent semantic models with a deep structure that project queries and documents into a common low-dimensional space where the relevance of a document given a query is readily computed as the distance between them. The proposed deep structured semantic models are discriminatively trained by maximizing the conditional likelihood of the clicked documents given a query using the clickthrough data. To make our models applicable to large-scale Web search applications, we also use a technique called word hashing, which is shown to effectively scale up our semantic models to handle large vocabularies which are common in such tasks. The new models are evaluated on a Web document ranking task using a real-world data set. Results show that our best model significantly outperforms other latent semantic models, which were considered state-of-the-art in the performance prior to the work presented in this paper.},
publisher = {ACM International Conference on Information and Knowledge Management (CIKM)},
url = {http://approjects.co.za/?big=en-us/research/publication/learning-deep-structured-semantic-models-for-web-search-using-clickthrough-data/},
}