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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。
Aiming at the problem of cold start in the collaborative filtering recommendation algorithm, a novel microblog topic recommendation algorithm based on artificial bee colony is proposed. The fitness function is constructed based on the weibo topic popularity, user characteristics, user preferences and start time, and then the fitness value is solved. Finally, we recommend the weibo topic to the user based on the fitness value. Compared with CF, ACO-CF and PSO-CF, this algorithm reduces the MAE value, which shows that it can effectively solve the cold start problem in the collaborative filtering recommendation algorithm and can improve the accuracy of the recommendation.