Microblogging community is different from conventional social networks and e-commerce systems for its low user activity, data sparsity and dynamic of user-interests. Because of these challenges, conventional recommendation algorithms cannot get desirable performance in microblogging community. This paper proposes a novel recommendation algorithm based on Bayesian personalized ranking (BPR) by modeling user' s implicit feedbacks in microblogging community. The proposed algorithm collects implicit feedbacks in the form of microblogs pairs and uses them as training pairs to learn users' interest. This paper defines a confidential score for each microblogs pair based on the time user received it. Microblogs pairs with shorter interval time have higher confidential score and thus have much more impact on user's interest. Experiments on two real-world microblogs datasets show that the recommendation algorithm outperforms all the baselines.%微博不同于传统的社会网络和电子商务网站,存在用户活跃程度低,微博数据稀疏和用户兴趣动态变化等特点,将传统推荐算法应用于微博推荐时,效果并不理想.提出了一种基于贝叶斯个性化排序的微博推荐算法,对用户进行个性化微博推荐.该基于贝叶斯个性化排序的微博推荐算法,以微博对的形式提取微博系统中的隐式信息,对这些微博对进行学习,从而得到用户对不同微博的兴趣值.根据每条微博发出的时间,估计每条微博对的可信度.发出时间越接近的微博对,它的可信度就越高,并且对用户的兴趣值影响就越大.在新浪微博的真实数据上进行实验和评测,结果表明该基于贝叶斯个性化排序的微博推荐算法相比于对比算法,在进行微博推荐时有更好的效果.
展开▼
