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Erschienen in:
Mining Recent High-Utility Patterns from Temporal Databases with Time-Sensitive Constraint Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Power of Bosom Friends, POI Recommendation by Learning Preference of Close Friends and Similar Users

verfasst von : Mu-Yao Fang, Bi-Ru Dai

Erschienen in: Big Data Analytics and Knowledge Discovery

Verlag: Springer International Publishing

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Abstract

With the emergence of social networks, mining interesting information from the social media datasets becomes a popular research direction. Previous researches on social networks, such as POI (point of interest) recommendation, usually ignore the social tie strength between users. If we can further consider the closeness between friends in the analysis, it is possible to improve the results. Therefore, in this paper, we focus on analyzing the social tie strength between users in the location-based social network. The proposed method analyzes the movement of users and the interaction between them by the spatial-temporal data. Furthermore, the social relationship structure is also taken into consideration for the calculation of the social tie strength. Finally, the location list for POI recommendation will be constructed accordingly. Experimental results show that the proposed method significantly outperforms the competitor on both precision and recall.

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Vorheriges Kapitel A Maximum Dimension Partitioning Approach for Efficiently Finding All Similar Pairs
Nächstes Kapitel Online Anomaly Energy Consumption Detection Using Lambda Architecture
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Metadaten
Titel
Power of Bosom Friends, POI Recommendation by Learning Preference of Close Friends and Similar Users
verfasst von
Mu-Yao Fang
Bi-Ru Dai
Copyright-Jahr
2016
Buch
Big Data Analytics and Knowledge Discovery

Print ISBN: 978-3-319-43945-7

Electronic ISBN: 978-3-319-43946-4

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-43946-4_12