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Multi-scale broad collaborative filtering for personalized recommendation
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AI
DOI：10.1016/j.knosys.2023.110853.png)
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Abstract
En 中文
Recently, neighborhood-based collaborative filtering has been increasingly used in personalized recommender systems. However, inevitably, the neighborhood selection is based on a single scale, i.e. selecting a fixed number of the nearest users/items. To solve this problem, we propose a new recommender system called Multi-scale Broad Collaborative Filtering (MBCF). The main contribution lies in designing a multi-scale collaborative vector for capturing the rich information from different numbers of the nearest users/items. However, it is undesirable to input the low-dimensional multiscale collaborative vector directly into the Deep Neural Networks (DNNs), which can easily lead to overfitting. For this reason, instead of DNNs, the Broad Learning System (BLS) is adopted as the mapping function to learn the complex nonlinear relationships between users and items, which can avoid the above problems while obtaining very satisfactory recommendation performance. Extensive experiments on eight benchmark datasets demonstrate the effectiveness of the proposed MBCF algorithm.
Keywords:
Recommender system
Multi-scale
Collaborative filtering
Broad learning system
Neural network
Journal
K
IF:
7.6
Papers:
1.2W
Citations:
4.5W