Enhancing Recommendation with Automated Tag Taxonomy Construction in Hyperbolic Space

The sparse interactions between users and items on the web have aggravated the difficulty of their representations in recommender systems. Existing approaches leverage tags to alleviate the data sparsity problem, so as to enhance the performance and interpretability of recommendation. However, directly using flat item tags fails to fully exploit the hierarchical relations in data, but tag taxonomies are not always available. To this end, we propose TaxoRec to jointly construct a tag taxonomy automatically and perform recommendation accurately in hyperbolic space. Specifically, we first leverage hyperbolic space and enable the optimization of a discrete taxonomy structure via a representation-aware scoring function and an adaptive clustering algorithm, and preserve the hierarchical structure for interpretability. Then, we propose to capture the complex relations among users, items, and tags in a unified hyperbolic metric space, where a novel tag-enhanced aggregation mechanism and tag-enhanced metric learning algorithm for users and items are defined. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed TaxoRec framework
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https://github.com/Melinda315/TaxoRec, which constantly achieves an average of 7.76% improvement over the state-of-the-art baselines regarding both Recall and NDCG metrics. Insightful case studies also show that our automatically constructed tag taxonomies are highly accurate and interpretable.