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Search Personalization with Embeddings

Vu, Thanh; Nguyen, Dat Quoc; Johnson, Mark; Song, Dawei and Willis, Alistair (2017). Search Personalization with Embeddings. In: 39th European Conference on Information Retrieval (ECIR 2017), 8-13 April 2017, Aberdeen, Scotland UK.

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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.

Item Type: Conference or Workshop Item
Keywords: Information Retrieval; Search Personalization; Embeddings;
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 48159
Depositing User: Thanh Vu
Date Deposited: 19 Jan 2017 13:40
Last Modified: 01 Feb 2017 14:10
URI: http://oro.open.ac.uk/id/eprint/48159
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