Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph

Abstract Natural language inference (NLI) is the basic task of many applications such as question answering and paraphrase recognition. Existing methods have solved the key issue of how the NLI model can benefit from external knowledge. Inspired by this, we attempt to further explore the following two problems: (1) how to make better use of external knowledge when the total amount of such knowledge is constant and (2) how to bring external knowledge to the NLI model more conveniently in the application scenario. In this paper, we propose a novel joint training framework that consists of a modified graph attention network, called the knowledge graph attention network, and an NLI model. We demonstrate that the proposed method outperforms the existing method which introduces external knowledge, and we improve the performance of multiple NLI models without additional external knowledge.

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LkeRec: Toward Lightweight End-to-End Joint Representation Learning for Building Accurate and Effective Recommendation

ACM Transactions on Information Systems ◽

10.1145/3486673 ◽

2022 ◽

Vol 40 (3) ◽

pp. 1-28

Author(s):

Surong Yan ◽

Kwei-Jay Lin ◽

Xiaolin Zheng ◽

Haosen Wang

Keyword(s):

Side Information ◽

Dimensional Space ◽

Representation Learning ◽

Knowledge Graph ◽

Learning Framework ◽

Relation Type ◽

End To End ◽

Low Dimensional ◽

Item Knowledge ◽

Explicit And Implicit Knowledge

Explicit and implicit knowledge about users and items have been used to describe complex and heterogeneous side information for recommender systems (RSs). Many existing methods use knowledge graph embedding (KGE) to learn the representation of a user-item knowledge graph (KG) in low-dimensional space. In this article, we propose a lightweight end-to-end joint learning framework for fusing the tasks of KGE and RSs at the model level. Our method proposes a lightweight KG embedding method by using bidirectional bijection relation-type modeling to enable scalability for large graphs while using self-adaptive negative sampling to optimize negative sample generating. Our method further generates the integrated views for users and items based on relation-types to explicitly model users’ preferences and items’ features, respectively. Finally, we add virtual “recommendation” relations between the integrated views of users and items to model the preferences of users on items, seamlessly integrating RS with user-item KG over a unified graph. Experimental results on multiple datasets and benchmarks show that our method can achieve a better accuracy of recommendation compared with existing state-of-the-art methods. Complexity and runtime analysis suggests that our method can gain a lower time and space complexity than most of existing methods and improve scalability.

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