基于协同过滤的在线教学视频推荐方法

在目前的在线教学系统中,用户对教学视频的选择具有一定的盲目性,根据这一特点,提出了一种基于协同过滤的在线教学视频推荐方法,可以将用户可能感兴趣的教学视频＂推＂给用户;首先将用户的观看视频纪录整理并保存至数据库中,依据各用户历史播放纪录以及用户的基本信息的兴趣差异来查询邻居用户,然后利用这些邻居用户的视频观看记录基于协同过滤的方法进行教学视频的推荐;改进了传统协同过滤推荐方法中普遍存在的稀疏性（Sparse）和冷启始（Cold Start）等问题,因此能使推荐更为精确;另外,通过用户是否观看所推荐的视频,可以对系统做出隐性评价以修正系统的参数,以提高推荐的准确性。

Online Teaching Video Recommendation Method Based on Collaborative Filtering

In current online teaching system, the users have certain blindness on choosing teaching videos, according to this characteristic, online teaching video recommendation method based on collaborative filtering is poi vid nted out, which can recommend the teaching videos in which the users are interested to other users. Firstly, the eo-watehing records are saved in database, and neighboring users are searched based on historic viewing records of each user and the interest difference between each user in basic information, then teaching video recommendation is conducted according to video-watching records of these neighboring users based on collaborative filtering method. This method improves the Sparse and Cold Start commonly existed in traditional collaborative filtering recommendation method and makes the recommendation more accurate. In addition, whether the users watch the recommended videos can make implicit evaluation on the system to revise the parameters of the system and to improve the accuracy of the recommendation.