Search Personalization with Embeddings
This work addresses search personalization for users of web search engines, but it is incremental as it builds on existing embedding methods for user profiles.
The paper tackled the problem of search personalization by proposing an embedding approach to learn user profiles based on topical interests, which improved search engine performance and outperformed other baselines in experiments on query logs from a major commercial web search engine.
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.