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中文摘要：
随着电子商务的发展，基于协同过滤的推荐算法越来越受欢迎，与此同时，该算法的缺陷也越来越明显，如数据稀疏性、系统可扩展性等。为此，提出一种混合型推荐算法。该混合算法首先利用谱聚类方法，根据图谱理论将聚类问题转换为图的分割问题，寻找相似数据群；然后利用扩展逻辑回归的朴素贝叶斯算法对聚类结果建立预测模型；最后使用增量式更新的方法，在不全部重新训练模型的基础上，对模型进行局部修改。实验结果表明，该算法较传统的协同过滤算法在一定程度上克服了数据稀疏性和冷启动问题，降低了计算复杂度，并且具有更好的准确性和可扩展性。
英文摘要：
Collaborative fihering-based recommender algorithm have become extremely popular in recent years, due to the development of E-commerce. By the way, it has some limitations such as, sparsity, scalability. This paper developed a hybird rec： ommendation method to overcome the limitations of CF. Firstly, the hybrid method used spectral clustering to transform the cluster to segment, and found similar segments. Secondly, the hybrid method applied the extended logistic regression on naive Bayes to build prediction model. Finally, the hybrid method used the increment update schemes to refresh the ratings and improved the precision of the system. The experimental results demonstrate the proposed approach overcomes sparsity and scalability problem to a certain extent, has higher accuracy,lower complexity, scalability than traditional collaborative filtering.
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