TL;DR
This paper investigates hyperbolic space's advantages in recommender systems, especially for tail items, and introduces HICF, a novel hyperbolic collaborative filtering method that enhances recommendation performance for both head and tail items.
Contribution
It provides a detailed analysis of hyperbolic versus Euclidean models in recommendation, and proposes HICF, a hyperbolic learning method that improves recommendation accuracy for diverse item types.
Findings
Hyperbolic models emphasize tail items more than Euclidean models.
Head items receive modest attention in hyperbolic space, but can be improved.
HICF outperforms existing models in recommendation tasks.
Abstract
Considering the prevalence of the power-law distribution in user-item networks, hyperbolic space has attracted considerable attention and achieved impressive performance in the recommender system recently. The advantage of hyperbolic recommendation lies in that its exponentially increasing capacity is well-suited to describe the power-law distributed user-item network whereas the Euclidean equivalent is deficient. Nonetheless, it remains unclear which kinds of items can be effectively recommended by the hyperbolic model and which cannot. To address the above concerns, we take the most basic recommendation technique, collaborative filtering, as a medium, to investigate the behaviors of hyperbolic and Euclidean recommendation models. The results reveal that (1) tail items get more emphasis in hyperbolic space than that in Euclidean space, but there is still ample room for improvement; (2)…
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