Community search has been wildly studied to retrieve relevant cluster from attributed graph for a given set of sample nodes. However, existing well-studied methods aim at searching a community containing sample nodes, and fail to capture communities without sample nodes but are similar with user preference deduced from the given sample nodes. To this end, we propose a community search method that is capable of finding multi-communities with user's preference using few given sample nodes and simultaneously identify outliers in attributed network. The method is termed as Integrating user Preference into Multi-community and Outliers Search (IPMOS), which collaborates user's preference into the process of searching to find interesting clusters of the entire network. Particularly, the strategy of truncated random walk is first used to expand few sample nodes. And then, the average partition similarity is defined based on exploring strategy to infer attribute subspace as user's latent interest. Finally, multiple communities and outliers in the whole network are detected via fractional-core and structural constraints. Extensive experiments on several synthetic networks and real-world networks with different scales and subjects demonstrate the effectiveness and efficiency of our approach.