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Reinforcement Routing on Proximity Graph for Efficient Recommendation

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/bimj.4710290717openalex-snapshot
Preparata, F. P., M. I. Shamos: Computaional Geometry. An Introduction. Springer‐Verlag, New York ‐ Berlin ‐ Heidelberg ‐ Tokyo 1985, xii, 390 pp., 231 Figs., DM 148.‐
inferredcites →https://doi.org/10.1007/978-1-4615-3626-0openalex-snapshot
Vector Quantization and Signal Compression
inferredcites →https://doi.org/10.1007/s00453-014-9885-5openalex-snapshot
Randomized Partition Trees for Nearest Neighbor Search
inferredcites →https://doi.org/10.1007/s10994-018-5711-7openalex-snapshot
Improved maximum inner product search with better theoretical guarantee using randomized partition trees
inferredcites →https://doi.org/10.1007/s10994-021-05962-3openalex-snapshot
Topic extraction from extremely short texts with variational manifold regularization
inferredcites →https://doi.org/10.1016/b978-0-08-051584-7.50011-5openalex-snapshot
Vector Quantization
inferredcites →https://doi.org/10.1016/j.aiopen.2021.01.001openalex-snapshot
Graph neural networks: A review of methods and applications
inferredcites →https://doi.org/10.1016/j.is.2013.10.006openalex-snapshot
Approximate nearest neighbor algorithm based on navigable small world graphs
inferredcites →https://doi.org/10.1109/cvpr.1993openalex-snapshot
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
inferredcites →https://doi.org/10.1109/cvpr.2009.5206651openalex-snapshot
Active learning for large multi-class problems
inferredcites →https://doi.org/10.1109/cvpr.2013.237openalex-snapshot
Fast, Accurate Detection of 100,000 Object Classes on a Single Machine
inferredcites →https://doi.org/10.1109/cvpr.2013.379openalex-snapshot
Optimized Product Quantization for Approximate Nearest Neighbor Search
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/icdm.2008.22openalex-snapshot
Collaborative Filtering for Implicit Feedback Datasets
inferredcites →https://doi.org/10.1109/mc.2009.263openalex-snapshot
Matrix Factorization Techniques for Recommender Systems
inferredcites →https://doi.org/10.1109/spire.1999.796589openalex-snapshot
Searching in metric spaces by spatial approximation
inferredcites →https://doi.org/10.1109/tip.2017.2668218openalex-snapshot
Stacked Learning to Search for Scene Labeling
inferredcites →https://doi.org/10.1109/tkde.2019.2951386openalex-snapshot
Discrete Matrix Factorization and Extension for Fast Item Recommendation
inferredcites →https://doi.org/10.1109/tkde.2020.2964232openalex-snapshot
Product Quantized Collaborative Filtering
inferredcites →https://doi.org/10.1109/tpami.2012.193openalex-snapshot
Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval
inferredcites →https://doi.org/10.1109/tpami.2018.2889473openalex-snapshot
Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs
inferredcites →https://doi.org/10.1109/tse.1979.234200openalex-snapshot
Multidimensional Binary Search Trees in Database Applications
inferredcites →https://doi.org/10.1109/wacv.2007.18openalex-snapshot
Clustering Billions of Images with Large Scale Nearest Neighbor Search
inferredcites →https://doi.org/10.1145/2043932.2043987openalex-snapshot
Applications of the conjugate gradient method for implicit feedback collaborative filtering
inferredcites →https://doi.org/10.1145/2339530.2339677openalex-snapshot
Maximum inner-product search using cone trees
inferredcites →https://doi.org/10.1145/2396761.2396831openalex-snapshot
Efficient retrieval of recommendations in a matrix factorization framework
inferredcites →https://doi.org/10.1145/2645710.2645741openalex-snapshot
Speeding up the Xbox recommender system using a euclidean transformation for inner-product spaces
inferredcites →https://doi.org/10.1145/3035918.3064009openalex-snapshot
FEXIPRO
inferredcites →https://doi.org/10.1145/3219819.3219971openalex-snapshot
Accurate and Fast Asymmetric Locality-Sensitive Hashing Scheme for Maximum Inner Product Search
inferredcites →https://doi.org/10.1145/3331184.3331196openalex-snapshot
Bayesian Personalized Feature Interaction Selection for Factorization Machines
inferredcites →https://doi.org/10.1145/3366423.3380151openalex-snapshot
LightRec: A Memory and Search-Efficient Recommender System
inferredcites →https://doi.org/10.1145/3366423.3380187openalex-snapshot
Personalized Ranking with Importance Sampling
inferredcites →https://doi.org/10.1145/3408317openalex-snapshot
Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry, and Fusion
inferredcites →https://doi.org/10.1145/3411754openalex-snapshot
Block-Aware Item Similarity Models for Top- N Recommendation
inferredcites →https://doi.org/10.1145/3442381openalex-snapshot
Proceedings of the Web Conference 2021
inferredcites →https://doi.org/10.1145/3447548.3467315openalex-snapshot
Reinforced Anchor Knowledge Graph Generation for News Recommendation Reasoning
inferredcites →https://doi.org/10.1145/3447548.3467441openalex-snapshot
Online Additive Quantization
inferredcites →https://doi.org/10.1145/355744.355745openalex-snapshot
An Algorithm for Finding Best Matches in Logarithmic Expected Time
inferredcites →https://doi.org/10.1145/361002.361007openalex-snapshot
Multidimensional binary search trees used for associative searching
inferredcites →https://doi.org/10.5555/944919.944966openalex-snapshot
A neural probabilistic language model

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1016/j.inffus.2024.102468openalex-snapshot
Relational Fusion-based Stock Selection with Neural Recursive Ordinary Differential Equation Networks
inferred← citeshttps://doi.org/10.1080/08839514.2025.2487417openalex-snapshot
Personalized Recommendation System of E-Commerce in the Digital Economy Era: Enhancing Social Connections with Graph Attention Networks
inferred← citeshttps://doi.org/10.1109/icde65448.2025.00087openalex-snapshot
Effective and General Distance Computation for Approximate Nearest Neighbor Search
inferred← citeshttps://doi.org/10.1109/iconstem60960.2024.10568864openalex-snapshot
Cutting-Edge Travel Planner: Intelligent Route Recommendation System using Enhanced Learning Scheme with AI Principles
inferred← citeshttps://doi.org/10.1145/3543507.3583422openalex-snapshot
Cooperative Retriever and Ranker in Deep Recommenders
inferred← citeshttps://doi.org/10.1145/3568022openalex-snapshot
A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
inferred← citeshttps://doi.org/10.1145/3580305.3599406openalex-snapshot
Learning Balanced Tree Indexes for Large-Scale Vector Retrieval
inferred← citeshttps://doi.org/10.1145/3589282openalex-snapshot
High-Dimensional Approximate Nearest Neighbor Search: with Reliable and Efficient Distance Comparison Operations
inferred← citeshttps://doi.org/10.1145/3604915.3608887openalex-snapshot
Demystifying Recommender Systems: A Multi-faceted Examination of Explanation Generation, Impact, and Perception
inferred← citeshttps://doi.org/10.1145/3654970openalex-snapshot
RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search
inferred← citeshttps://doi.org/10.1145/3704998openalex-snapshot
Market-aware Long-term Job Skill Recommendation with Explainable Deep Reinforcement Learning
inferred← citeshttps://doi.org/10.1145/3733723.3733724openalex-snapshot
How to Mine Potentially Popular Items? A Reverse MIPS-based Approach
inferred← citeshttps://doi.org/10.1145/3769783openalex-snapshot
Dynamically Detect and Fix Hardness for Efficient Approximate Nearest Neighbor Search
inferred← citeshttps://doi.org/10.14778/3681954.3681959openalex-snapshot
RoarGraph: A Projected Bipartite Graph for Efficient Cross-Modal Approximate Nearest Neighbor Search
inferred← citeshttps://doi.org/10.14778/3725688.3725705openalex-snapshot
Maximum Inner Product is Query-Scaled Nearest Neighbor
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