融合协同过滤和主题模型的弹幕视频推荐算法

针对弹幕视频网站通常不为用户提供评分功能并且使用弹幕池策略以降低存储压力导致的缺少用户评分和无法准确建模用户主题的问题，提出融合主题模型和协同过滤的弹幕视频推荐算法（DRCFT）。首先根据用户行为构造隐式评分矩阵，并得到项目评分相似度；其次建立LDA主体模型，生成项目主题，计算项目主题相似度；再加权生成综合项目相似度，由此结合评分矩阵计算用户—项目预测评分；然后对项目主题相似度矩阵进行sigmoid惩罚，结合用户历史记录得到用户—项目主题相似度；最后将用户—项目主题相似度作为权重，与预测评分相乘，得到最终的预测评分。对比实验表明，该算法能够得到合理的推荐结果，提高推荐的准确性。

Danmaku video recommendation combining collaborative filtering and topic model

To solve the problem of lacking user ratings and inaccuracy of user topics model for danmaku video website, this paper proposed a danmaku video recommendation algorithm combing topic model and collaborative filtering(DRCFT). Firstly, it constructed an implicit rating matrix with user behavior, and obtained the similarity of project rating. Secondly, it established an LDA topic model to generate project topics and calculate the similarity of project topics. Thirdly, it generated a comprehensive project similarity, and calculated the user-project prediction score. Then, through sigmoid penalty, it established the new similarity matrix of project topics and got user-project topic similarity combined with user history behavior. Finally, it toke user-project topic similarity as weight to multiply with prediction score to get the final prediction score. The comparison experiments show that the algorithm can get reasonable recommendation results and improve the accuracy of recommendation.