@inproceedings{4b6a01c859e84c13ac1e7305920ae0d3,
title = "Search personalization with embeddings",
abstract = "Recent research has shown that the performance of search personalization depends on the richness of user profiles which normally represent the user{\textquoteright}s topical interests. In this paper, we propose a new embedding approach to learning user profiles, where users are embedded on a topical interest space. We then directly utilize the user profiles for search personalization. Experiments on query logs from a major commercial web search engine demonstrate that our embedding approach improves the performance of the search engine and also achieves better search performance than other strong baselines.",
author = "Thanh Vu and Nguyen, \{Dat Quoc\} and Mark Johnson and Dawei Song and Alistair Willis",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 39th European Conference on Information Retrieval, ECIR 2017 ; Conference date: 08-04-2017 Through 13-04-2017",
year = "2017",
doi = "10.1007/978-3-319-56608-5\_54",
language = "English",
isbn = "9783319566078",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "598--604",
editor = "Claudia Hauff and Jose, \{Joemon M.\} and Dyaa Albakour and Altingovde, \{Ismail Sengor\} and John Tait and Dawei Song and Stuart Watt",
booktitle = "Advances in Information Retrieval - 39th European Conference on IR Research, ECIR 2017, Proceedings",
address = "Germany",
}