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Auteur Renyao Chen |
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Location-aware neural graph collaborative filtering / Shengwen Li in International journal of geographical information science IJGIS, vol 36 n° 8 (August 2022)
[article]
Titre : Location-aware neural graph collaborative filtering Type de document : Article/Communication Auteurs : Shengwen Li, Auteur ; Chenpeng Sun, Auteur ; Renyao Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1550 - 1574 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] comportement
[Termes IGN] données localisées des bénévoles
[Termes IGN] filtrage d'information
[Termes IGN] jeu de données
[Termes IGN] noeud
[Termes IGN] point d'intérêt
[Termes IGN] réseau neuronal de graphesRésumé : (auteur) Collaborative filtering (CF) is initiated by representing users and items as vectors and seeks to describe the relationship between users and items at a profound level, thus predicting users’ preferred behavior. To address the issue that previous research ignored higher-order geographical interactions hidden in users’ historical behaviors, this paper proposes a location-aware neural graph collaborative filtering model (LA-NGCF), which incorporates location information of items for improving prediction performance. The model characterizes the interactions between items based on spatial decay law from a graph perspective and designs two strategies to capture the interaction effects of users and items considering node heterogeneity. An optimized loss function with spatial distances of items is also developed in the model. Extensive experiments are conducted on three publicly available real-world datasets to examine the effectiveness of our model. Results show that LA-NGCF achieves competitive performances compared with several state-of-the-art models, which suggests that location information of items is beneficial for improving the performance of personalized recommendations. This paper offers an approach to incorporate weighted interactions between items into CF algorithms and enriches the methods of utilizing geographical information for artificial intelligence applications. Numéro de notice : A2022-592 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2073594 Date de publication en ligne : 11/05/2022 En ligne : https://doi.org/10.1080/13658816.2022.2073594 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101292
in International journal of geographical information science IJGIS > vol 36 n° 8 (August 2022) . - pp 1550 - 1574[article]