MNI: An enhanced multi-task neighborhood interaction model for recommendation on knowledge graph

To alleviate the data sparsity and cold start problems for collaborative filtering in recommendation systems, side information is usually leveraged by researchers to improve the recommendation performance. The utility of knowledge graph regards the side information as part of the graph structure and...

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Autores principales:	Xintao Ma, Liyan Dong, Yuequn Wang, Yongli Li, Hao Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d6c77c0f53d2478db014905a0c8693f7
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id	oai:doaj.org-article:d6c77c0f53d2478db014905a0c8693f7
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spelling	oai:doaj.org-article:d6c77c0f53d2478db014905a0c8693f72021-11-04T06:49:35ZMNI: An enhanced multi-task neighborhood interaction model for recommendation on knowledge graph1932-6203https://doaj.org/article/d6c77c0f53d2478db014905a0c8693f72021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8553089/?tool=EBIhttps://doaj.org/toc/1932-6203To alleviate the data sparsity and cold start problems for collaborative filtering in recommendation systems, side information is usually leveraged by researchers to improve the recommendation performance. The utility of knowledge graph regards the side information as part of the graph structure and gives an explanation for recommendation results. In this paper, we propose an enhanced multi-task neighborhood interaction (MNI) model for recommendation on knowledge graphs. MNI explores not only the user-item interaction but also the neighbor-neighbor interactions, capturing a more sophisticated local structure. Besides, the entities and relations are also semantically embedded. And with the cross&compress unit, items in the recommendation system and entities in the knowledge graph can share latent features, and thus high-order interactions can be investigated. Through extensive experiments on real-world datasets, we demonstrate that MNI outperforms some of the state-of-the-art baselines both for CTR prediction and top-N recommendation.Xintao MaLiyan DongYuequn WangYongli LiHao ZhangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xintao Ma Liyan Dong Yuequn Wang Yongli Li Hao Zhang MNI: An enhanced multi-task neighborhood interaction model for recommendation on knowledge graph
description	To alleviate the data sparsity and cold start problems for collaborative filtering in recommendation systems, side information is usually leveraged by researchers to improve the recommendation performance. The utility of knowledge graph regards the side information as part of the graph structure and gives an explanation for recommendation results. In this paper, we propose an enhanced multi-task neighborhood interaction (MNI) model for recommendation on knowledge graphs. MNI explores not only the user-item interaction but also the neighbor-neighbor interactions, capturing a more sophisticated local structure. Besides, the entities and relations are also semantically embedded. And with the cross&compress unit, items in the recommendation system and entities in the knowledge graph can share latent features, and thus high-order interactions can be investigated. Through extensive experiments on real-world datasets, we demonstrate that MNI outperforms some of the state-of-the-art baselines both for CTR prediction and top-N recommendation.
format	article
author	Xintao Ma Liyan Dong Yuequn Wang Yongli Li Hao Zhang
author_facet	Xintao Ma Liyan Dong Yuequn Wang Yongli Li Hao Zhang
author_sort	Xintao Ma
title	MNI: An enhanced multi-task neighborhood interaction model for recommendation on knowledge graph
title_short	MNI: An enhanced multi-task neighborhood interaction model for recommendation on knowledge graph
title_full	MNI: An enhanced multi-task neighborhood interaction model for recommendation on knowledge graph
title_fullStr	MNI: An enhanced multi-task neighborhood interaction model for recommendation on knowledge graph
title_full_unstemmed	MNI: An enhanced multi-task neighborhood interaction model for recommendation on knowledge graph
title_sort	mni: an enhanced multi-task neighborhood interaction model for recommendation on knowledge graph
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/d6c77c0f53d2478db014905a0c8693f7
work_keys_str_mv	AT xintaoma mnianenhancedmultitaskneighborhoodinteractionmodelforrecommendationonknowledgegraph AT liyandong mnianenhancedmultitaskneighborhoodinteractionmodelforrecommendationonknowledgegraph AT yuequnwang mnianenhancedmultitaskneighborhoodinteractionmodelforrecommendationonknowledgegraph AT yonglili mnianenhancedmultitaskneighborhoodinteractionmodelforrecommendationonknowledgegraph AT haozhang mnianenhancedmultitaskneighborhoodinteractionmodelforrecommendationonknowledgegraph
_version_	1718445119760236544

MNI: An enhanced multi-task neighborhood interaction model for recommendation on knowledge graph

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