  Search personalization aims to tailor search results to each specific user
based on the user's personal interests and preferences (i.e., the user
profile). Recent research approaches to search personalization by modelling the
potential 3-way relationship between the submitted query, the user and the
search results (i.e., documents). That relationship is then used to personalize
the search results to that user. In this paper, we introduce a novel embedding
model based on capsule network, which recently is a breakthrough in deep
learning, to model the 3-way relationships for search personalization. In the
model, each user (submitted query or returned document) is embedded by a vector
in the same vector space. The 3-way relationship is described as a triple of
(query, user, document) which is then modeled as a 3-column matrix containing
the three embedding vectors. After that, the 3-column matrix is fed into a deep
learning architecture to re-rank the search results returned by a basis ranker.
Experimental results on query logs from a commercial web search engine show
that our model achieves better performances than the basis ranker as well as
strong search personalization baselines.
