Abstract
Knowledge graphs (KGs) have been proven to be effective for improving the performance of recommender systems. KGs can store rich side information and relieve the data sparsity problem. There are many linked attributes between entity pairs (e.g., items and users) in KGs, which can be called multiple-step relation paths. Existing methods do not sufficiently exploit the information encoded in KGs. In this paper, we propose MRP2Rec to explore various semantic relations in multiple-step relation paths to improve recommendation performance. The knowledge representation learning approach is used in our method to learn and represent multiple-step relation paths, and they are further utilized to generate prediction lists by inner products in top-K recommendations. Experiments on two real-world datasets demonstrate that our model achieves higher performance compared with many state-of-the-art baselines.
Highlights
Recommender systems (RS) have become increasingly important for presenting information to users that meets their personalized preferences
Inspired by [32], different semantic relations of paths are expected to be captured from Knowledge graphs (KGs), and high-quality user/item representation can be learned for accurate user profiling and personalized recommendation
The data sparsity problem is relieved by coding various relation semantics in user and item representation, and the recommendation performance can be improved and exceed many state-of-the-art baselines
Summary
Recommender systems (RS) have become increasingly important for presenting information to users that meets their personalized preferences. It usually suffers from the data sparsity problem. To improve the performance of RS, researchers have proposed utilizing side information [1], [2] to enrich data. Traditional methods [3], [4] use supervised learning methods for encoding side information into item representations to predict user behaviours. According to the idea that paths connecting entity pairs have various latent semantic features that can be used to represent entities, exploring paths in KGs can promote user preference
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