@inproceedings{b3c4f839b8b84b4cbdb9549e28b5bccf,
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'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",
year = "2017",
doi = "10.1007/978-3-319-56608-5_54",
language = "English",
isbn = "9783319566078",
series = "Lecture notes in computer science",
publisher = "Springer, Springer Nature",
pages = "598--604",
editor = "Jose, {Joemon M.} and Claudia Hauff and Altıngovde, {Ismail Sengor} and Dawei Song and Dyaa Albakour and Stuart Watt and John Tait",
booktitle = "Advances in information retrieval",
address = "United States",
note = "39th European Conference on Information Retrieval, ECIR 2017 ; Conference date: 08-04-2017 Through 13-04-2017",
}