Multi-dimensional Adaptive Collaborative Filtering Recommendation Algorithm
Qing Zhu · Journal of Chinese Computer Systems · 2011
Collaborative filtering(CF) is one of the most important algorithms applied in e-commerce recommendation systems.The traditional methods are inefficient when the user rating data is extremely sparse.In order to overcome the limitations,a novel algorithm named MACF(Multi-dimensional Adaptive Collaborative Filtering Recommendation Algorithm) is proposed in this paper.The MACF algorithm creatively combines three recommendation models: user-based CF,item-based CF and review-based CF.It successfully integrates opinion mining technology with collaborative filtering algorithm.In addition,a dynamic measurement approach would help determine the weight of three dimensions: user,item and review,and hence get the final prediction result.The experimental results show that MACF can effectively alleviate the dataset sparsity problem and achieve better prediction accuracy compared to other well-performing collaborative filtering algorithms.