New Recommendation Algorithm of Valuation Filling Based on Community Filtering

Abstract

For the data sparsity problem in the traditional collaborative filtering algorithm, this paper proposes a new recommendation algorithm of valuation filling based on community filtering. This algorithm confirms the "user rating scale" and "commodity popularity" in the similar user group of each kind, so as to achieve more accurate valuation calculation. And then it improves the quality of recommendation. Finally, the new algorithm’s feasibility and effectiveness are verified by specific experiments, and it has better recommendation effect on sparse data sets.