Content-aware Collaborative Filtering in Point-ofInterest Recommendation Systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F19%3A00334915" target="_blank" >RIV/68407700:21240/19:00334915 - isvavai.cz</a>

Výsledek na webu

<a href="https://hi.kkui.fei.tuke.sk/daz2019/DaZ_WIKT_2019_Zbornik.pdf" target="_blank" >https://hi.kkui.fei.tuke.sk/daz2019/DaZ_WIKT_2019_Zbornik.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Content-aware Collaborative Filtering in Point-ofInterest Recommendation Systems

Popis výsledku v původním jazyce

With the availability of the vast amount of users and Location-based social networks, the problem of POI recommendations has been widely studied and received significant research attention in the last years. While previous works of POI recommendation mostly focused on investigating the spatial, temporal, and social influence, the use of additional content information has not been directionally studied. In this paper, we propose the content-aware matrix factorization method based on incorporating POI attributes and categories information. We propose two variants of the algorithm that can work with an explicit and implicit feedback. Experimental results show that the proposed method improves the quality of recommendation and outperforms most state-ofthe-art collaborative filtering algorithms.

Název v anglickém jazyce

Content-aware Collaborative Filtering in Point-ofInterest Recommendation Systems

Popis výsledku anglicky

With the availability of the vast amount of users and Location-based social networks, the problem of POI recommendations has been widely studied and received significant research attention in the last years. While previous works of POI recommendation mostly focused on investigating the spatial, temporal, and social influence, the use of additional content information has not been directionally studied. In this paper, we propose the content-aware matrix factorization method based on incorporating POI attributes and categories information. We propose two variants of the algorithm that can work with an explicit and implicit feedback. Experimental results show that the proposed method improves the quality of recommendation and outperforms most state-ofthe-art collaborative filtering algorithms.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

DATA A ZNALOSTI & WIKT 2019

ISBN

978-80-553-3354-0

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

20-25

Název nakladatele

Technická univerzita v Košiciach

Místo vydání

Košice

Místo konání akce

Košice

Datum konání akce

10. 10. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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