Search Personalization with Embeddings

TL;DR

This paper introduces a novel embedding method for user profiles in search personalization, embedding users in a topical interest space to enhance search performance, validated through experiments on real query logs.

Contribution

The paper presents a new embedding approach for user profiles that improves search personalization performance over existing methods.

Findings

Embedding approach enhances search engine performance.

Outperforms strong baseline methods.

Effective in real-world query log experiments.

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.