Community recommendation has become increasingly important in sifting valuable communities from massive amounts of communities on the Internet. In recent years novel recommendation is attracting attention, because of the limitation of accurate recommendation which purely pursues accuracy. Existing novel recommendation methods are not suitable for Web community as they fail to utilize unique features of Web community, including the social network established by interactions between users, and the topics of Web community. In this paper, a novel recommendation method, NovelRec, is proposed to suggest communities that users have not seen but are potentially interested in, in order to better broaden users' horizons and improve the development of communities. Specifically, the method explores neighborhood relationships and topical associations from the aforementioned features. First, NovelRec identifies candidate communities for users based on neighborhood relationships between users, and computes accuracy of the candidates using topical associations between users. Next, NovelRec computes novelty of the candidates based on a new metric of user-community distance, and the distance metric is defined by associations between users and communities on both user neighborhood and topic taxonomy. Finally, NovelRec balances novelty with accuracy for the candidates to improve the overall recommendation quality. Experimental results on a real data set of Douban communities show that the proposed method outperforms competitors on the recommendation novelty, and guarantees the recommendation accuracy.