# Search Personalization with Embeddings

arXiv: https://arxiv.org/abs/1612.03597 · HTML: https://mlsanity.com/paper/1612.03597/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2016.

## Built on by (most cited first, 2 of 2)

- [Query Rewriting via Cycle-Consistent Translation for E-Commerce Search](https://mlsanity.com/paper/2103.00800/) (2021)
- [USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence](https://mlsanity.com/paper/2109.15012/) (2021)

## Abstract (arXiv)

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.