A Movie Recommendation System Based on Hybrid Double Cluster

发布时间：2023-06-03 17:32

　　移动互联网的发展给人们的生活带来了巨大的便利。互联网上存在大量的信息可以供用户参考和查阅。然而信息量的快速增加也带来了一些问题。用户想从种类繁多的信息中快速找到自己需要的信息变得非常困难,导致大量的资源不能得到充分的利用,利用率降低。为了解决这些问题,个性化推荐系统应运而生。在互联网的各种应用中,推荐系统扮演着技术驱动的角色。目前主流的电子商务推荐系统大多使用协同过滤算法实现个性化推荐。它根据用户的喜好以及历史评分数据,挖掘出用户可能喜欢的内容并生成推荐。协同过滤推荐算法为电子商务个性化推荐系统的发展做出了重要的贡献。然而,协同过滤算法也存在一定的缺点,包括数据稀疏性和冷启动的问题。这些问题一直制约着推荐制度的实践。尤其是在当今大数据的情况下,这些问题变得更加突出。协同过滤算法主要是利用用户对商品的评分,通过计算相似度找到相似物品,然后进行推荐。然而在大数据的情况下,物品越来越多,用户越来越多,但每个用户可能仅仅对几个项目进行了评价。尽管一些用户拥有比较多的评分信息,但对于整个数据矩阵来说,它仍然太少了。因而用户-评分矩阵在典型情况下都是稀疏的。例如在淘宝、亚马逊、当当网等典型的利用个...

【文章页数】：87 页

【学位级别】：硕士

【文章目录】：
Acknowledgements
abstract
1 Introduction
    1.1 Topic background and research significance
    1.2 Existing problems and main research content
    1.3 Related work
    1.4 The key technologies of recommendation system
        1.4.1 Personalized recommendation system
        1.4.2 Main personalized recommendation algorithms
        1.4.3 Research on collaborative filtering algorithm
        1.4.4 Summary
    1.5 The structure of paper
2 Collaborative filtering algorithm based on double clustering algorithm
    2.1 Problem description
    2.2 The idea of improved algorithm
    2.3 The advantages of the improved algorithm
    2.4 The recommendation model of KSDC-CF algorithm
    2.5 Algorithm implementation
        2.5.1 Data padding
        2.5.2 Singular value decomposition
        2.5.3 Double clustering
        2.5.4 Generating predictive recommendations
    2.6 Experimental results
        2.6.1 Experimental setup
        2.6.2 Experimental dataset
        2.6.3 Experimental design
        2.6.4 Evaluation metrics
        2.6.5 Experimental results and analysis
3 Content-based and double clustering collaborative filtering
    3.1 Problem description
    3.2 The idea of hybrid algorithm
    3.3 The advantages of the hybrid algorithm
    3.4 The recommendation model of hybrid algorithm
    3.5 Algorithm implementation
        3.5.1 Item attribute similarity calculation
        3.5.2 Generating the user attribute rating matrix
        3.5.3 User feature similarity calculation
        3.5.4 Clustering
        3.5.5 Generating predictive recommendations
    3.6 Experimental result and analysis
        3.6.1 Experimental dataset
        3.6.2 Experimental design
        3.6.3 Experimental results and analysis
4 A Movie recommendation system
    4.1 System architecture design
    4.2 System functions
        4.2.1 User functional requirements
        4.2.2 Administrator functional requirement
    4.3 Database design
    4.4 Environmental setup
    4.5 Algorithm process
    4.6 System implementation
5 Conclusion and future work
    5.1 Conclusion
    5.2 Future work
References
Appendix A 摘要



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