Recommend Algorithm Combined User-user Neighborhood Approach with Latent Factor Model
The item-item neighborhood model became very volatile for current items rapidly replaced, such as online article and news items. And the neighborhood model faces the problem of data sparsity and cold start. The factor model can alleviate data sparseness problem, but it does not take the historical behavior data into consideration. Therefore, in this paper, we proposes a recommendation algorithm based on user-user neighborhood model and latent factor model, which can make accuracy improved significantly and can effectively address the problem of data sparsity. When the number of neighborhoods k increases, the accuracy of the algorithm has improved. The experiment result shows that this approach is correct and feasible.
KeywordsRecommend system User-user model Latent factor model
This work was supported by the National Natural Science Foundation of China (Grant No. 61203117) and Yao is the corresponding author.
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