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Electronic Commerce Research

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30.11.2019

Leveraging friend and group information to improve social recommender system

verfasst von: Jianshan Sun, Rongrong Ying, Yuanchun Jiang, Jianmin He, Zhengping Ding

Erschienen in: Electronic Commerce Research | Ausgabe 1/2020

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Abstract

In recent years, we have witnessed a flourish of social commerce services. Online users can easily share their experiences on products or services with friends. Social recommender systems are employed to tailor right products for user needs. However, existing recommendation methods try to consider the social information to improve the recommendation performance while they do not differ the impact of different social information and do not have deep analysis on social information. In this paper, we propose a social recommendation framework to leverage the friend and group information to extend the traditional BPR model from different perspectives. Through a detailed experiment on LAST.FM data set, we find that the proposed methods are effective in improving the recommendation accuracy and we also have a good understanding for the impact of friend and group information on recommendation performance.

Vorheriger Artikel Design of sweepstakes-based social media marketing for online customer engagement

Nächster Artikel Microlending on mobile social credit platforms: an exploratory study using Philippine loan contracts

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Titel: Leveraging friend and group information to improve social recommender system
verfasst von: Jianshan Sun
Rongrong Ying
Yuanchun Jiang
Jianmin He
Zhengping Ding
Publikationsdatum: 30.11.2019
Verlag: Springer US
Erschienen in: Electronic Commerce Research / Ausgabe 1/2020
Print ISSN: 1389-5753
Elektronische ISSN: 1572-9362
DOI: https://doi.org/10.1007/s10660-019-09390-3

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