Abstract
Two-way cooperative collaborative filtering (CF) has been known to be crucial for binary market basket data. We propose an improved two-way logistic regression approach, a Pearson correlation-based score, a random forests (RF) R-square-based score, an RF Pearson correlation-based score, and a CF scheme based on the RF R-square-based score. The main idea is to utilize as much predictive information as possible within the two-way prediction in order to cope with the cold-start problem. All of the proposed methods work better than the existing two-way cooperative CF approach in terms of the experimental results.
Highlights
User similarity measures in collaborative filtering (CF) are crucial for recommendations [1,2]
We propose a PCA+LR two-way 2, a Pearson correlation-based score, an random forests (RF) R-square-based score, an RF Pearson correlation-based score, and a CF scheme for the RF R-square-based score for two-way cooperative CF for binary market basket data
The experimental results show that the proposed two-way cooperative CF approaches work better than the existing PCA+LR two-way 1
Summary
User similarity measures in collaborative filtering (CF) are crucial for recommendations [1,2]. Mild and Reutterer [18] proposed using the Pearson correlation-based approach rather than the user-based CF leveraging on the Pearson correlation for binary market basket data [19]. The PCA+LR may not perform well, because the principal components are ineffective when there are insufficient numbers of either users or items in the binary market basket data, which can be modeled as a high-dimensional cold-start problem [23]. As Hwang and Jun [23] show, the Pearson correlation-based and random forest regression approaches can outperform the PCA+LR for the high dimensional cold-start problem. We propose an improved two-way logistic regression approach, a Pearson correlation-based score, an RF R-square-based score, an RF Pearson correlation-based score, and a CF scheme based on the RF R-square-based score for two-way cooperative CF for binary market basket data.
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