Collaborative Filtering Recommendation Algorithm under High Sparse Data Clustering
Wu Yue · 2008
In order to resolve the poor-quality of recommendation in collaborative filtering recommendation algorithms in case of the high sparse dataset,this paper proposes a novel algorithm named item-based clustering recommendation algorithm(IBCRA).One of characteristics in the IBCRA is that it has considered the properties of data sparse difference and item category clustering within user-item dataset.Specifically,on the basis of high-dimensions data clustering algorithms,the IBCRA algorithm uses the rating data sparse difference and item categories in the rating dataset to construct a measuring formula for calculating dataset difference,where the formula is used for item clustering in user-item rating array.Then the IBCRA calculates item similarity and searches for k-nearest neighbors of target item based on the outcome of item clustering.Finally it forecasts the ratings for those no rating item in dataset and so generates recommendations.The experimental results show the IBCRA has improved the recommendation quality in collaborative filtering recommendation.The comparative experiments and parameter sensitivity analysis also show,in perspective of the accuracy and speed of convergence,the IBCRA also outperforms the collaborative filtering recommendation algorithm with all items based algorithm.