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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.

Sponsored search has been recognized as one of the major internet monetization solutions for commercial search engines. There are generally three types of participants in this online advertising problem, who are search users, advertisers and publishers. Though previous studies have proposed to optimize for different participants independently, it is underexplored how to optimize for all participants in a unified framework and in a systematic way . In this paper, we propose to model the ad ranking problem in sponsored search as a M ulti-Objective Optimization (MOO) problem for all participants. We show that many previous studies are sp ecial cases of the M OO framework. Taking advantage from the Pareto solution set of MOO, we can easily find more optimized solutions with significant improvement in one objective and minor sacrifice in others. This enables a more flexible way for us to tradeoff among different participants, i.e. objective functions, in sponsored search. Besides the empirical studies for comparing MOO with related previous sponsored search studies, we provide the insightful applications of M OO framework, which is a prediction model to help users determine the tradeoff parameters among different objective functions. Experimental results show the outstanding performance of the proposed prediction model for p arameter selection in ad ranking optimization.

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