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Hybrid fuzzy collaborative filtering: an integration of item-based and user-based clustering techniques

机译：混合模糊协作筛选：基于项目和基于用户的聚类技术的集成

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摘要

Clustering is one of the successful approaches of the model-based collaborative filtering techniques that deals with the problem of sparsity and provides quality recommendations. In the proposed work, fuzzy c-means clustering technique is adopted in order to produce item-based clusters as well as user-based clusters. Subsequently, collaborative filtering technique explores the item-based and user-based clusters and generates the list of item-based and user-based predictions, respectively. Further, to enhance the quality of recommendations, a novel weighted hybrid scheme is designed which integrates the user-based and item-based predictions to capture the influence of each active user towards item-based and user-based predictions. The proposed schemes are further categorised on the basis of re-clustering and without re-clustering under different similarity measures over sparse and dense datasets. The experimental results reveal that the variants of the proposed hybrid schemes consistently generate better results in comparison with the corresponding variants of proposed user-based schemes and the traditional item-based schemes.
机译：群集是基于模型的协作过滤技术的成功方法之一，这些滤波技术涉及稀疏性问题并提供质量建议。在所提出的工作中，采用模糊C-Means聚类技术，以便生产基于项目的群集以及基于用户的集群。随后，协同滤波技术探讨基于项目的基于用户的群集，并分别生成基于项目和基于用户的预测的列表。此外，为了增强推荐的质量，设计了一种新的加权混合方案，其集成了基于用户的基于项目的基于项目的预测，以捕获每个活动用户对基于项目和基于用户的预测的影响。所提出的方案在重新聚类的基础上进一步进行分类，并且在不在不同相似度下的稀疏和密度数据集的不同相似性测量下没有重新聚类。实验结果表明，与所提出的基于用户的方案和基于传统项目的方案的相应变体相比，所提出的杂交方案的变体始终产生更好的结果。

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