  The key to personalized search is to build the user profile based on
historical behaviour. To deal with the users who lack historical data, group
based personalized models were proposed to incorporate the profiles of similar
users when re-ranking the results. However, similar users are mostly found
based on simple lexical or topical similarity in search behaviours. In this
paper, we propose a neural network enhanced method to highlight similar users
in semantic space. Furthermore, we argue that the behaviour-based similar users
are still insufficient to understand a new query when user's historical
activities are limited. To tackle this issue, we introduce the friend network
into personalized search to determine the closeness between users in another
way. Since the friendship is often formed based on similar background or
interest, there are plenty of personalized signals hidden in the friend network
naturally. Specifically, we propose a friend network enhanced personalized
search model, which groups the user into multiple friend circles based on
search behaviours and friend relations respectively. These two types of friend
circles are complementary to construct a more comprehensive group profile for
refining the personalization. Experimental results show the significant
improvement of our model over existing personalized search models.
