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 Apr 2017, Aberdeen, Scotland UK.

DOI: https://doi.org/10.1007/978-3-319-56608-5_54

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.

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About

• Item ORO ID
• 48159
• Item Type
• Conference or Workshop Item
• Keywords
• Information Retrieval; Search Personalization; Embeddings;
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
  Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Research Group
• Centre for Research in Computing (CRC)
• Related URLs
• • https://arxiv.org/abs/1612.03597
• Depositing User
• Thanh Vu