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Graph Embedding Based Recommendation Techniques on the Knowledge Graph

Published: 09 July 2017 Publication History

Abstract

This paper presents a novel, graph embedding based recommendation technique. The method operates on the knowledge graph, an information representation technique alloying content-based and collaborative information. To generate recommendations, a two dimensional embedding is developed for the knowledge graph. As the embedding maps the users and the items to the same vector space, the recommendations are then calculated on a spatial basis. Regarding to the number of cold start cases, precision, recall, normalized Cumulative Discounted Gain and computational resource need, the evaluation shows that the introduced technique delivers a higher performance compared to collaborative filtering on top-n recommendation lists. Our further finding is that graph embedding based methods show a more stable performance in the case of an increasing amount of user preference information compared to the benchmark method.

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cover image ACM Conferences
UMAP '17: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization
July 2017
456 pages
ISBN:9781450350679
DOI:10.1145/3099023
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 09 July 2017

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Author Tags

  1. graph embedding
  2. graph layout
  3. knowledge graph
  4. recommendation technique
  5. recommender system

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Overall Acceptance Rate 162 of 633 submissions, 26%

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Cited By

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  • (2024)Research on the Application of Knowledge Mapping Technology in Vocational Skills Training Demand Forecasting and Curriculum DesigningApplied Mathematics and Nonlinear Sciences10.2478/amns-2024-28189:1Online publication date: 9-Oct-2024
  • (2024)Towards Knowledge-Aware and Deep Reinforced Cross-Domain Recommendation Over Collaborative Knowledge GraphIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.339126836:11(7171-7187)Online publication date: Nov-2024
  • (2024)Recommender systems based on neuro-symbolic knowledge graph embeddings encoding first-order logic rulesUser Modeling and User-Adapted Interaction10.1007/s11257-024-09417-xOnline publication date: 26-Sep-2024
  • (2024)Introduction to the Industrial Application of Semantic TechnologiesOntology-Based Development of Industry 4.0 and 5.0 Solutions for Smart Manufacturing and Production10.1007/978-3-031-47444-6_2(23-65)Online publication date: 1-Jan-2024
  • (2023)Recommendation Systems for e-Shopping: Review of Techniques for Retail and Sustainable MarketingSustainability10.3390/su15231615115:23(16151)Online publication date: 21-Nov-2023
  • (2023)Reinforcement Learning Based Path Exploration for Sequential Explainable RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.323774135:11(11801-11814)Online publication date: 1-Nov-2023
  • (2022)Building a Graph-Based Recommender Using Community EmbeddingsProceedings of the 2022 8th International Conference on Computer Technology Applications10.1145/3543712.3543727(121-127)Online publication date: 12-May-2022
  • (2022)Knowledge-aware Recommendations Based on Neuro-Symbolic Graph Embeddings and First-Order Logical RulesProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3551484(616-621)Online publication date: 12-Sep-2022
  • (2022)Item Relationship Graph Neural Networks for E-CommerceIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.306087233:9(4785-4799)Online publication date: Sep-2022
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