Radial Neighborhood Smoothing Recommender System

• URL: http://arxiv.org/abs/2507.09952v1
• Date: Mon, 14 Jul 2025 06:01:58 GMT
• Title: Radial Neighborhood Smoothing Recommender System
• Authors: Zerui Zhang, Yumou Qiu,
• Abstract summary: Radial Neighborhood Estimator (RNE) is proposed to construct neighborhoods based on overlapped and partially overlapped user-item pairs.<n>RNE achieves superior performance compared to existing collaborative filtering and matrix factorization methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recommender systems inherently exhibit a low-rank structure in latent space. A key challenge is to define meaningful and measurable distances in the latent space to capture user-user, item-item, user-item relationships effectively. In this work, we establish that distances in the latent space can be systematically approximated using row-wise and column-wise distances in the observed matrix, providing a novel perspective on distance estimation. To refine the distance estimation, we introduce the correction based on empirical variance estimator to account for noise-induced non-centrality. The novel distance estimation enables a more structured approach to constructing neighborhoods, leading to the Radial Neighborhood Estimator (RNE), which constructs neighborhoods by including both overlapped and partially overlapped user-item pairs and employs neighborhood smoothing via localized kernel regression to improve imputation accuracy. We provide the theoretical asymptotic analysis for the proposed estimator. We perform evaluations on both simulated and real-world datasets, demonstrating that RNE achieves superior performance compared to existing collaborative filtering and matrix factorization methods. While our primary focus is on distance estimation in latent space, we find that RNE also mitigates the ``cold-start'' problem.

Related papers

• Structured Sampling for Robust Euclidean Distance Geometry [6.422262171968397]
  This paper addresses the problem of estimating the positions of points from distance measurements corrupted by sparse outliers.<n>We propose a novel algorithm powered by Nystr"om method and robust principal component analysis.<n>Our algorithm achieves accurate recovery with a modest number of anchors, even in the presence of high levels of sparse outliers.
  arXiv  Detail & Related papers  (2024-12-14T03:42:18Z)
• Robust Second-order LiDAR Bundle Adjustment Algorithm Using Mean Squared Group Metric [5.153195958837083]
  We propose a novel mean square group metric (MSGM) to build the optimization objective in the LiDAR BA algorithm. By integrating a robust kernel function, the metrics involved in the BA algorithm are reweighted, and thus enhancing the robustness of the solution process.
  arXiv  Detail & Related papers  (2024-09-03T12:53:39Z)
• Learning conditional distributions on continuous spaces [0.0]
  We investigate sample-based learning of conditional distributions on multi-dimensional unit boxes. We employ two distinct clustering schemes: one based on a fixed-radius ball and the other on nearest neighbors. We propose to incorporate the nearest neighbors method into neural network training, as our empirical analysis indicates it has better performance in practice.
  arXiv  Detail & Related papers  (2024-06-13T17:53:47Z)
• Vanishing Point Estimation in Uncalibrated Images with Prior Gravity Direction [82.72686460985297]
  We tackle the problem of estimating a Manhattan frame. We derive two new 2-line solvers, one of which does not suffer from singularities affecting existing solvers. We also design a new non-minimal method, running on an arbitrary number of lines, to boost the performance in local optimization.
  arXiv  Detail & Related papers  (2023-08-21T13:03:25Z)
• On the Effective Usage of Priors in RSS-based Localization [56.68864078417909]
  We propose a Received Signal Strength (RSS) fingerprint and convolutional neural network-based algorithm, LocUNet. In this paper, we study the localization problem in dense urban settings. We first recognize LocUNet's ability to learn the underlying prior distribution of the Rx position or Rx and transmitter (Tx) association preferences from the training data, and attribute its high performance to these.
  arXiv  Detail & Related papers  (2022-11-28T00:31:02Z)
• Far Away in the Deep Space: Dense Nearest-Neighbor-Based Out-of-Distribution Detection [33.78080060234557]
  Nearest-Neighbors approaches have been shown to work well in object-centric data domains. We show that nearest-neighbor approaches also yield state-of-the-art results on dense novelty detection in complex driving scenes.
  arXiv  Detail & Related papers  (2022-11-12T13:32:19Z)
• iSDF: Real-Time Neural Signed Distance Fields for Robot Perception [64.80458128766254]
  iSDF is a continuous learning system for real-time signed distance field reconstruction. It produces more accurate reconstructions and better approximations of collision costs and gradients.
  arXiv  Detail & Related papers  (2022-04-05T15:48:39Z)
• The Probabilistic Normal Epipolar Constraint for Frame-To-Frame Rotation Optimization under Uncertain Feature Positions [53.478856119297284]
  We introduce the probabilistic normal epipolar constraint (PNEC) that overcomes the limitation by accounting for anisotropic and inhomogeneous uncertainties in the feature positions. In experiments on synthetic data, we demonstrate that the novel PNEC yields more accurate rotation estimates than the original NEC. We integrate the proposed method into a state-of-the-art monocular rotation-only odometry system and achieve consistently improved results for the real-world KITTI dataset.
  arXiv  Detail & Related papers  (2022-04-05T14:47:11Z)
• Canny-VO: Visual Odometry with RGB-D Cameras based on Geometric 3D-2D Edge Alignment [85.32080531133799]
  This paper reviews the classical problem of free-form curve registration and applies it to an efficient RGBD visual odometry system called Canny-VO. Two replacements for the distance transformation commonly used in edge registration are proposed: Approximate Nearest Neighbour Fields and Oriented Nearest Neighbour Fields. 3D2D edge alignment benefits from these alternative formulations in terms of both efficiency and accuracy.
  arXiv  Detail & Related papers  (2020-12-15T11:42:17Z)
• Nystr\"om Approximation with Nonnegative Matrix Factorization [4.990119940008071]
  We show that the proximity clustering problem can be effectively formulated as the Nystr"om approximation problem. We implement the Nystr"om approximation based on a landmark based Nonnegative Matrix Factorization (NMF) process.
  arXiv  Detail & Related papers  (2020-08-07T23:52:59Z)
• Making Affine Correspondences Work in Camera Geometry Computation [62.7633180470428]
  Local features provide region-to-region rather than point-to-point correspondences. We propose guidelines for effective use of region-to-region matches in the course of a full model estimation pipeline. Experiments show that affine solvers can achieve accuracy comparable to point-based solvers at faster run-times.
  arXiv  Detail & Related papers  (2020-07-20T12:07:48Z)

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.