Knowledge-based Multiple Adaptive Spaces Fusion for Recommendation

• URL: http://arxiv.org/abs/2308.15244v1
• Date: Tue, 29 Aug 2023 12:11:16 GMT
• Title: Knowledge-based Multiple Adaptive Spaces Fusion for Recommendation
• Authors: Meng Yuan, Fuzhen Zhuang, Zhao Zhang, Deqing Wang and Jin Dong
• Abstract summary: We propose a knowledge-based multiple adaptive spaces fusion method for recommendation, namely MCKG. Unlike existing methods that solely adopt a specific manifold, we introduce the unified space that is compatible with hyperbolic, euclidean and spherical spaces. In addition, we propose a geometry-aware optimization strategy which enables the pull and push processes benefited from both hyperbolic and spherical spaces.
• Score: 35.20583774988951
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Since Knowledge Graphs (KGs) contain rich semantic information, recently there has been an influx of KG-enhanced recommendation methods. Most of existing methods are entirely designed based on euclidean space without considering curvature. However, recent studies have revealed that a tremendous graph-structured data exhibits highly non-euclidean properties. Motivated by these observations, in this work, we propose a knowledge-based multiple adaptive spaces fusion method for recommendation, namely MCKG. Unlike existing methods that solely adopt a specific manifold, we introduce the unified space that is compatible with hyperbolic, euclidean and spherical spaces. Furthermore, we fuse the multiple unified spaces in an attention manner to obtain the high-quality embeddings for better knowledge propagation. In addition, we propose a geometry-aware optimization strategy which enables the pull and push processes benefited from both hyperbolic and spherical spaces. Specifically, in hyperbolic space, we set smaller margins in the area near to the origin, which is conducive to distinguishing between highly similar positive items and negative ones. At the same time, we set larger margins in the area far from the origin to ensure the model has sufficient error tolerance. The similar manner also applies to spherical spaces. Extensive experiments on three real-world datasets demonstrate that the MCKG has a significant improvement over state-of-the-art recommendation methods. Further ablation experiments verify the importance of multi-space fusion and geometry-aware optimization strategy, justifying the rationality and effectiveness of MCKG.

Related papers

• On Probabilistic Pullback Metrics on Latent Hyperbolic Manifolds [5.724027955589408]
  This paper focuses on the hyperbolic manifold, a particularly suitable choice for modeling hierarchical relationships. We propose augmenting the hyperbolic metric with a pullback metric to account for distortions introduced by theVM's nonlinear mapping. Through various experiments, we demonstrate that geodesics on the pullback metric not only respect the geometry of the hyperbolic latent space but also align with the underlying data distribution.
  arXiv  Detail & Related papers  (2024-10-28T09:13:00Z)
• Scaling Riemannian Diffusion Models [68.52820280448991]
  We show that our method enables us to scale to high dimensional tasks on nontrivial manifold. We model QCD densities on $SU(n)$ lattices and contrastively learned embeddings on high dimensional hyperspheres.
  arXiv  Detail & Related papers  (2023-10-30T21:27:53Z)
• Alignment and Outer Shell Isotropy for Hyperbolic Graph Contrastive Learning [69.6810940330906]
  We propose a novel contrastive learning framework to learn high-quality graph embedding. Specifically, we design the alignment metric that effectively captures the hierarchical data-invariant information. We show that in the hyperbolic space one has to address the leaf- and height-level uniformity which are related to properties of trees.
  arXiv  Detail & Related papers  (2023-10-27T15:31:42Z)
• HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularization [52.369435664689995]
  We introduce a textitHyperbolic Regularization powered Collaborative Filtering (HRCF) and design a geometric-aware hyperbolic regularizer. Specifically, the proposal boosts optimization procedure via the root alignment and origin-aware penalty. Our proposal is able to tackle the over-smoothing problem caused by hyperbolic aggregation and also brings the models a better discriminative ability.
  arXiv  Detail & Related papers  (2022-04-18T06:11:44Z)
• Where are we in embedding spaces? A Comprehensive Analysis on Network Embedding Approaches for Recommender Systems [30.32394422015953]
  This paper provides theoretical analysis and empirical results on when and where to use hyperbolic space and hyperbolic embeddings in recommender systems. We evaluate our answers by comparing the performance of Euclidean space and hyperbolic space on different latent space models. We propose a new metric learning based recommendation method called SCML and its hyperbolic version HSCML.
  arXiv  Detail & Related papers  (2021-05-19T03:46:41Z)
• Switch Spaces: Learning Product Spaces with Sparse Gating [48.591045282317424]
  We propose Switch Spaces, a data-driven approach for learning representations in product space. We introduce sparse gating mechanisms that learn to choose, combine and switch spaces. Experiments on knowledge graph completion and item recommendations show that the proposed switch space achieves new state-of-the-art performances.
  arXiv  Detail & Related papers  (2021-02-17T11:06:59Z)
• Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality Reduction [48.73525876467408]
  We propose a novel technique for hyperspectral subspace analysis. The technique is called joint and progressive subspace analysis (JPSA) Experiments are conducted to demonstrate the superiority and effectiveness of the proposed JPSA on two widely-used hyperspectral datasets.
  arXiv  Detail & Related papers  (2020-09-21T16:29:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.