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GeoInformatica
Erschienen in: GeoInformatica 3/2017

15.03.2017

Personalized location recommendation by aggregating multiple recommenders in diversity

verfasst von: Ziyu Lu, Hao Wang, Nikos Mamoulis, Wenting Tu, David W. Cheung

Erschienen in: GeoInformatica | Ausgabe 3/2017

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Abstract

Location recommendation is an important feature of social network applications and location-based services. Most existing studies focus on developing one single method or model for all users. By analyzing data from two real location-based social networks (Foursquare and Gowalla), in this paper we reveal that the decisions of users on place visits depend on multiple factors, and different users may be affected differently by these factors. We design a location recommendation framework that combines results from various recommenders that consider different factors. Our framework estimates, for each individual user, the underlying influence of each factor to her. Based on the estimation, we aggregate suggestions from different recommenders to derive personalized recommendations. Experiments on Foursquare and Gowalla show that our proposed method outperforms the state-of-the-art methods on location recommendation.
Vorheriger Artikel Index-supported pattern matching on tuples of time-dependent values
Nächster Artikel Popularity-aware collective keyword queries in road networks

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Fußnoten
2
Cosine similarity and Pearson’s correlation coefficient have very close performance in our experiments, thus we just use cosine similarity for simplicity.
 
3
Note that sometimes a recommender may fail to generate a list of length 10. For example, FCF (R 2) requires that the target user has some friends but loners do exist in LBSNs. In such cases, we complement the length-10 list with the most popular locations.
 
4
It is worth mentioning that a trivial implementation of LURWA is to put equal weights on component recommenders. This, however, usually leads to very bad recommendations due to the diversities we studied in Section 2 (Figs. 1–2). Indeed, the essence of learning is to identify those good recommenders out of a large population of bad ones, with regard to some individual user.
 
5
POI recommendation on sparse datasets has relatively low precision and recall values [15, 42]. We focus on comparing methods’ relative performance, instead of the absolute performances.
 
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Metadaten
Titel
Personalized location recommendation by aggregating multiple recommenders in diversity
verfasst von
Ziyu Lu
Hao Wang
Nikos Mamoulis
Wenting Tu
David W. Cheung
Publikationsdatum
15.03.2017
Verlag
Springer US
Erschienen in
GeoInformatica / Ausgabe 3/2017
Print ISSN: 1384-6175
Elektronische ISSN: 1573-7624
DOI
https://doi.org/10.1007/s10707-017-0298-x

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