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提出了一种新的协同过滤模型,解决了不同用户在项目上,有相似的偏好、不同的评分习惯的问题。该模型可有效地改进传统协同过滤模型相似性度量方法,提高了用户相似性度量准确性。实验结果表明,该模型在个性化推荐系统应用中取得了较好的效果。
A new collaborative filtering model is proposed to solve the problem that different users have similar preferences and different scoring habits on the project. The model can effectively improve the similarity measure method of traditional collaborative filtering model and improve the user similarity measurement accuracy. Experimental results show that the model has achieved good results in the application of personalized recommendation system.