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中文摘要：
针对协同过滤推荐算法中存在的冷启动问题,文章提出一种融合人工蜂群的微博话题推荐算法。通过微博话题热度、用户特征、用户偏好和起始时间构建适应度函数,然后求解适应度值,最后根据适应度值对用户进行微博话题推荐。与CF、ACO-CF和PSO-CF三种算法相比,该算法降低了MAE值,说明它能够有效解决协同过滤推荐算法中的冷启动问题,并能提高推荐的准确性。
英文摘要：
Due to the cold-start problem that exists in the collaborative filtering recommendation algorithm,a topic recommendation algorithm combining Artificial Bee Colony（ABC）was introduced.This algorithm constructs the fitness function by heat of a topic in microblog,user characteristics,user preference,starting time,and computing the fitness value,finally,recommending the microblog topic to the users according to the fitness value.Compared with CF,ACO CF and PSO-CF algorithm,this algorithm reduced the value of MAE.It suggets that this method can solve the cold-start problem that exists in the collaborative filtering recommendation algorithm efficiently and improve the precision of the recommendation.
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