随着移动应用的急速增长，手机助手等移动应用获取平台也面临着信息过载的问题.面对大量的移动应用，用户很难找到最适合的；而另一方面，长尾应用淹没在资源池中不易被人所知.已有推荐方法多注重推荐准确率，忽视了多样性，推荐结果中多是下载量高的应用，使得推荐系统的数据积累越来越偏向于热门应用，导致长期的推荐效果越来越差.针对这一问题，首先改进了两种推荐方法，提出了将用户的主题模型和应用的主题模型与MF相结合的LDA_MF模型，以及将应用的标签信息和用户行为数据同时加以考虑的LDA_CF算法.为了结合不同算法的优点，在保证推荐准确率的条件下提升推荐结果的多样性，提出了融合LDA_MF，LDA_CF以及经典的基于物品的协同过滤模型的混合推荐算法.使用真实的大数据评测所提推荐算法，结果显示，所提推荐方法能够得到推荐多样性更好且准确率更高的结果.

With rapid growth of mobile applications, users of mobile app platforms are facing problem of information overload. Large number of apps make it difficult for users to find appropriate ones, while many long tail apps are submerged in the resource pool and are unknown to most users. Meanwhile, existing recommendation methods usually pay more attention to accuracy than diversity, making popular apps as most recommended items. As a result, the overall exposure rate of mobile apps is low as behavior data accumulated by the system is gradually biased towards popular apps, which leads to a poor recommendation performance in the long run. To solve this problem, this article first proposes two recommendation methods, named LDA_MF and LDA_CF, to improve existing methods. LDA_MF combines user topic model and app topic model with matrix factorization model MF, and LDA_CF takes both tag information of apps and user behavior data into consideration. In order to take advantages of different algorithms and increase the diversity of recommendation results without sacrificing accuracy, a hybrid recommendation algorithm is also provided to combine LDA_MF, LDA_CF and item-based collaborative filtering models. A large real data set is used to evaluate the proposed methods, and the results show that the presented approach achieves better diversity and good recommendation accuracy.

TP311

国家自然科学基金（71272029，71490724，61472426）；国家高技术研究发展计划（863）（2014AA015204）；北京市自然科学基金（4152026）