Titlebook: Data Mining and Big Data; Third International Ying Tan,Yuhui Shi,Qirong Tang Conference proceedings 2018 Springer International Publishing

Cuisine

小教堂

反應(yīng)

Adjustment to Empire, 1763–1770 attribute similarity and item similarity to improve the accuracy of similarity. Experiments using two reals datasets show that the proposed method relieves cold start and data sparsity issues and improves the prediction accuracy and recommendation quality.

Arctic

燒瓶

豐滿有漂亮

Explainable Matrix Factorization with Constraints on Neighborhood in the Latent Space change the method of calculation of the explainability matrix and consider the neighbors’ weight to further improve performance. We use the benchmark data set (MovieLens) to demonstrate the effectiveness of the proposed Neighborhood-based Explainable Matrix Factorization. And the result shows a great improvement for accuracy and explainability.

模仿

An Entropy-Based Similarity Measure for Collaborative Filteringmation entropy. Performance of the proposed measure is investigated through various experiments to find that it outperforms the existing similarity measures especially in a small-scaled sparse dataset.

誘惑

Three-Segment Similarity Measure Model for Collaborative Filtering attribute similarity and item similarity to improve the accuracy of similarity. Experiments using two reals datasets show that the proposed method relieves cold start and data sparsity issues and improves the prediction accuracy and recommendation quality.

FLUSH

消耗