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Enhanced Knowledge Graph Embedding for Multi-Task Recommendation via Integrating Attribute Information and High-Order Connectivity

Published: 24 January 2022 Publication History

Abstract

Recently, knowledge graph (KG) has been introduced into recommender systems as side information to mitigate problems of sparsity and cold start, which attracts growing attention. Among these works, the multi-task learning methods that learn KG-related tasks and recommendation tasks have greatly enhanced the effectiveness of the recommendation. However, existing works tend to ignore attribute triples in the real knowledge graph, which makes the research affected by the sparsity of KGs. General knowledge graph embedding methods in multi-task recommendation barely take direct relations between entities into account, that leads to the ignorance of rich information in high-order relations. In addition, the lack of consideration on the user attribute information makes recommendation less than satisfactory. In order to relieve these issues, we propose a model of enhanced knowledge graph embedding for recommendation based on the multi-task learning, HMKR. Our method alternately trains recommendation task considering user-side information and enhanced knowledge graph embedding task integrating attribute information and high-order connectivity. The experiments on the real dataset MovieLens verify the effectiveness of HMKR. Even in the sparse data scenarios, the performance of our model is also satisfactory.

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      IJCKG '21: Proceedings of the 10th International Joint Conference on Knowledge Graphs
      December 2021
      204 pages
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      Published: 24 January 2022

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      Author Tags

      1. Graph Neural Network
      2. Knowledge Graph
      3. Multi-Task Learning
      4. Recommender Systems

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