Query Expansion with Enriched User Profiles

Query expansion has been widely adopted in Web search as a way of tackling the ambiguity of queries.
Personalized search utilizing folksonomy data has demonstrated an extreme vocabulary mismatch problem that requires even
more effective query expansion methods. Co-occurrence statistics, tag-tag relationships and semantic matching approaches are
among those favored by previous research. However, user profiles which only contain a user’s past annotation information may
not be enough to support the selection of expansion terms, especially for users with limited previous activity with the system.
We propose a novel model to construct enriched user profiles with the help of an external corpus for personalized query
expansion. Our model integrates the current state-of-the-art text representation learning framework, known as word
embeddings, with topic models in two groups of pseudo-aligned documents. Based on user profiles, we build two novel query
expansion techniques. These two techniques are based on topical weights-enhanced word embeddings, and the topical
relevance between the query and the terms inside a user profile respectively. The results of an in-depth experimental
evaluation, performed on two real-world datasets using different external corpora, show that our approach outperforms
traditional techniques, including existing non-personalized and personalized query expansion methods.