MKNBL: Joint multi-channel knowledge-aware network and broad learning for sparse knowledge graph-based recommendation

Abstract

To address data sparsity and cold start problems in recommender systems, existing studies have utilized knowledge graph (KG) as side information to enhance the performance of recommendation. However, existing methods often explore side information only from one perspective, leading to limited expressive power in the representations of users and items. Some researchers have also combined broad learning system (BLS) with collaborative filtering (CF) to achieve efficient training. Yet the improvement of these approaches is influenced by the availability of data. These approaches perform poorly in cases of data sparsity. To solve these limitations, this study proposes a two-stage KG-based recommendation method (MKNBL) that integrates multi-channel knowledge-aware network (MKN) and BLS. The first stage focuses on exploring valuable side information. We adopt MKN to learn multi-view knowledge from KG. This enables recommender systems to widely utilize KG as side information to mitigate the limitations of data sparsity and cold start problems. The second stage focuses on efficient data enhancement learning. We employ BLS to enhance the valuable information discovered in the previous step, aiming to achieve better recommendation accuracy. Finally, we validate the high diversity and accuracy of MKNBL through experiments. Compared with other state-of-the-art (SOTA) methods, MKNBL achieves improvements of 7.50%, 3.06%, 5.35% and 3.07% in mean absolute error (MAE) in the Movielens-1M, Last.FM, Book-Crossing and Amazon datasets, respectively.