Search personalization with embeddings

Thanh Vu, Dat Quoc Nguyen*, Mark Johnson, Dawei Song, Alistair Willis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

32 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationAdvances in information retrieval
Subtitle of host publication39th European Conference on IR Research, ECIR 2017, Aberdeen, UK, April 8–13, 2017, proceedings
EditorsJoemon M. Jose, Claudia Hauff, Ismail Sengor Altıngovde, Dawei Song, Dyaa Albakour, Stuart Watt, John Tait
Place of PublicationCham
PublisherSpringer, Springer Nature
Number of pages7
ISBN (Electronic)9783319566085
ISBN (Print)9783319566078
Publication statusPublished - 2017
Event39th European Conference on Information Retrieval, ECIR 2017 - Aberdeen, United Kingdom
Duration: 8 Apr 201713 Apr 2017

Publication series

NameLecture notes in computer science
PublisherSpringer International Publishing AG
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other39th European Conference on Information Retrieval, ECIR 2017
Country/TerritoryUnited Kingdom
City Aberdeen


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