Determining Absolute Interpolation Weights for Neighborhood‐Based Collaborative Filtering
Hyoung Do Kim · Management Science and Financial Engineering · 2010
Despite the overall success of neighbor?based CF methods, there are some fundamental questions about neighbor selection and prediction mechanism including arbitrary similarity, over?fitting interpolation weights, no trust consideration between neighbours, etc. This paper proposes a simple method to compute absolute interpolation weights based on similarity values. In order to supplement the method, two schemes are additionally devised for high?quality neighbour selection and trust metrics based on co?ratings. The former requires that one or more neighbour’s similarity should be better than a pre?specified level which is higher than the minimum level. The latter gives higher trust to neighbours that have more co?ratings. Experimental results show that the proposed method outperforms the pure IBCF by about 8% improvement. Furthermore, it can be easily combined with other predictors for achieving better prediction quality.