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.
CITATION STYLE
Vu, T., Nguyen, D. Q., Johnson, M., Song, D., & Willis, A. (2017). Search personalization with embeddings. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10193 LNCS, pp. 598–604). Springer Verlag. https://doi.org/10.1007/978-3-319-56608-5_54
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