在海量网络资源中，用户为了寻找喜欢的视频往往需要进行频繁操作，个性化推荐服务可以有效解决该问题，但当前推荐服务准确度较低，为此，提出一种基于协作过滤的改进推荐方法。根据相似用户群，即邻居集的点播记录确定当前用户的推荐电影子集，挖掘当前用户的喜好，建立兴趣模型，并与推荐子集中的电影进行匹配，按匹配度高低进行推荐。对推荐电影子集进行分类，以适应家庭中多用户观看的情况。另外在系统运行初期采用相似影片的推荐以一定程度地缓解冷启动问题。实验结果表明，与现有协作过滤算法相比，改进推荐方法的推荐准确度有明显提高。%Users looking for a favorite video in vast amounts of network resources often need frequent operating, and personalized recommendation service can be an effective solution to this problem. Against the current lower recommendation accuracy, this paper presents an improved recommendation method based on collaborative filtering. It determines a movies subset that is recommended according to the past records of similar users namely neighbors set. Then it mines the preferences of current user, establishes the interest model of current user, and matches with the movies to recommend. Recommendation is in accordance with the level of matching degree. Afterwards, it classifies the film sets that are recommended to adapt to multi-user viewing in families. Additionally, it recommends similar films in the system early running to solve the cold-start problem in a certain degree. Experimental results show that the improved recommended method has distinct higher recommendation accuracy than the existing collaborative filtering algorithm.
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