基于用户邻域和主题的新颖性Web社区推荐方法

社区推荐从海量社区中为用户过滤出有价值的社区,变得越来越重要.新颖性推荐逐渐得到关注,因为单纯追求准确度的推荐结果存在局限性.已有新颖性推荐方法不适用于社区推荐,因其无法处理Web社区特性,包括社区成员用户通过交互形成的关系网络以及社区主题.提出了一种新颖性社区推荐方法NovelRec,向用户推荐其有潜在兴趣但不知道的社区,旨在拓展用户视野和推动社区发展.NovelRec基于用户交互网络中的邻域关系,利用用户之间在主题上的关联,计算候选社区对用户的准确度;根据用户与社区在邻域和主题上的关联,提出一种用户社区距离度量方式,并利用该距离计算候选社区的新颖度.在此基础上,NovelRec最终进行新颖性社区推荐,并兼顾推荐结果的准确性.真实数据集上的对比实验结果表明,NovelRec方法在新颖性上优于现有方法,同时能够保证推荐结果的准确性.

Novel Web Community Recommendation Based on User Neighborhood and Topic

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.