基于弹幕情感分析和主题模型的视频推荐算法

针对互联网上大量自制视频缺少用户评分、推荐准确率不高的问题，提出一种融合弹幕情感分析和主题模型的视频推荐算法（VRDSA）。首先，对视频的弹幕评论进行情感分析，得到视频的情感向量，之后基于情感向量计算视频之间的情感相似度；同时，基于视频的标签建立主题模型来得到视频标签的主题分布，并使用主题分布计算视频之间的主题相似度；接着，对视频的情感相似度和主题相似度进行融合得到视频间的综合相似度；然后，结合视频间的综合相似度和用户的历史记录得到用户对视频的偏好度；同时通过视频的点赞量、弹幕量、收藏数等用户互动指标对视频的大众认可度进行量化，并结合用户历史记录计算出视频的综合认可度；最后，基于用户对视频的偏好度和视频的综合认可度预测用户对视频的认可度，并生成个性化推荐列表来完成视频的推荐。实验结果表明，与融合协同过滤和主题模型的弹幕视频推荐算法（DRCFT）以及嵌入LDA主题模型的协同过滤算法（ULR-itemCF）相比，所提算法推荐的准确率平均提高了17.1%，召回率平均提高了22.9%，F值平均提高了22.2%。所提算法对弹幕进行情感分析，并融合主题模型，以此来完成对视频的推荐，并且充分挖掘了弹幕数据的情感性，使得推荐结果更加准确。

Video recommendation algorithm based on danmaku sentiment analysis and topic model

A large number of self-made videos on the Internet lack user ratings and the recommendation accuracies of them are not high. In order to solve the problems, a Video Recommendation algorithm based on Danmaku Sentiment Analysis and topic model (VRDSA) was proposed. Firstly, sentiment analysis was performed to video' danmaku comments to obtain the sentiment vectors of the videos, which were used to calculate the emotional similarities between the videos. At the same time, based on the tags of videos, a topic model was built to obtain the topic distribution of the video tags which was used to calculate the topic similarities between the videos. Secondly, the emotional similarities and topic similarities were merged to calculate synthesis similarities between the videos. Thirdly, combined with the comprehensive similarities between the videos and the user's history records, the user preference for videos was obtained. At the same time, the video public recognitions were quantified by user interaction metrics such as the number of likes, danmakus and collections, and the comprehensive recognitions of the videos were calculated by combining the user's history records. Finally, based on the user preference and video comprehensive recognitions, the user's recognitions of videos were predicted, and a personalized recommendation list was generated to complete the video recommendation. Experimental results show that, compared with Danmaku video Recommendation algorithm combing Collaborative Filtering and Topic model (DRCFT) and Unifying LDA (Latent Dirichlet Allocation) and Ratings Collaborative Filtering (ULR-itemCF), the proposed algorithm has the precision increased by 17.1% on average, the recall increased by 22.9% on average, and the F1 increased by 22.2% on average. The proposed algorithm completes the recommendation of videos by analyzing the sentiments of danmakus and integrating the topic model, and fully exploits the emotionality of damaku data to make the recommendation results more accurate.