基于聚类层次模型的视频推荐算法

Video recommendation algorithm based on clustering and hierarchical model

目前推荐系统存在评论数据稀疏、冷启动和用户体验度低等问题,为了提高推荐系统的性能和进一步改善用户体验,提出基于聚类层次模型的视频推荐算法。首先,从相关用户方面着手,通过近邻传播(AP)聚类分析得到相似用户,从而收集相似用户中的历史网络视频数据,进而形成视频推荐集合;其次,利用用户行为的历史数据计算出用户对视频的喜好值,再把视频的喜好值转换成视频的标签权重;最后,通过层次分析模型算出视频推荐集合中用户喜好视频的排序,产生推荐列表。基于Movie Lens Latest Dataset和You Tube视频评论文本数据集,实验结果表明所提算法在均方根误差和决策精度方面均表现出良好的性能。

Concerning the problem of data sparseness, cold start and low user experience of recommendation system, a video recommendation algorithm based on clustering and hierarchical model was proposed to improve the performance of recommendation system and user experience. Focusing on the user, similar users were obtained by analyzing Affiliation Propagation （AP） cluster, then historical data of online video of similar users was collected and a recommendation set of videos was geberated. Secondly, the user preference degree of a video was calculated and mapped into the tag weight of the video. Finally, a recommendation list of videos was generated by using analytic hierarchy model to calculate the ranking of user preference with videos. The experimental results on MovieLens Latest Dataset and YouTube video review text dataset show that the proposed algorithm has good performance in terms of Root-Mean-Square Error （RMSE） and the recommendation accuracy.