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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)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 8 (August 2022) . - pp 1550 - 1574 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 graphes Résumé : (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 [article]

Exploring spatiotemporal clusters based on extended kernel estimation methods / Jay Lee in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 5-6 (May-June 2017) . - pp 1154 - 1177 Titre : Exploring spatiotemporal clusters based on extended kernel estimation methods Type de document : Article/Communication Auteurs : Jay Lee, Auteur ; Junfang Gong, Auteur ; Shengwen Li, Auteur Année de publication : 2017 Article en page(s) : pp 1154 - 1177 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] données spatiotemporelles [Termes IGN] estimation par noyau [Termes IGN] exploration de données géographiques [Termes IGN] groupe [Termes IGN] implémentation (informatique) [Termes IGN] infraction [Termes IGN] Ohio (Etats-Unis) [Termes IGN] système d'information géographique Résumé : (auteur) We examined three different ways to integrate spatial and temporal data in kernel density estimation methods (KDE) to identify space–time clusters of geographic events. Spatial data and time data are typically measured in different units along respective dimensions. Therefore, spatial KDE methods require special extensions when incorporating temporal data to detect spatiotemporal clusters of geographical event. In addition to a real-world data set, we applied the proposed methods to simulated data that were generated through random and normal processes to compare results of different kernel functions. The comparison is based on hit rates and values of a compactness index with considerations of both spatial and temporal attributes of the data. The results show that the spatiotemporal KDE (STKDE) can reach higher hit rates while keeping identified hotspots compact. The implementation of these STKDE methods is tested using the 2012 crime event data in Akron, Ohio, as an example. The results show that STKDE methods reveal new perspectives from the data that go beyond what can be extracted by using the conventional spatial KDE. Numéro de notice : A2017-243 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1287371 En ligne : http://dx.doi.org/10.1080/13658816.2017.1287371 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85179 [article]

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