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Demand-Aware Opportunistic Spectrum Access: A Game-Theoretic Learning Approach Read chapter Read first chapter

2019 | OriginalPaper | Chapter

A Novel User Preference Prediction Model Based on Local User Interaction Network Topology

Authors : Siqing You, Li Zhou, Yan Liu, Hongjie Liu, Fei Xue

Published in: Communications, Signal Processing, and Systems

Publisher: Springer Singapore

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Abstract

As people’s decisions are influenced by their social relationships, social networks have been widely applied in user behavior analysis, preference prediction and recommendation. However, static social relationship in a network alone is insufficient to model interpersonal influence and predict user preferences. In this paper, we propose a local user interaction network topology (LUINT) model to calculate the social influence between neighbors, which takes into account three types of user interactions: “at” action, comment, and re-tweet. Moreover, we design and adopt a shortest path with maximum propagation (SPWMP) algorithm to model the influence propagation within the network. To evaluate our approach, experiments on data set KDD Cup 2012, Track 1 are conducted. The results indicate that the proposed model significantly outperforms the other benchmark methods in predicting preference of the users.

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Metadata
Title
A Novel User Preference Prediction Model Based on Local User Interaction Network Topology
Authors
Siqing You
Li Zhou
Yan Liu
Hongjie Liu
Fei Xue
Copyright Year
2019
Publisher
Springer Singapore
Book
Communications, Signal Processing, and Systems

Print ISBN: 978-981-10-6570-5

Electronic ISBN: 978-981-10-6571-2

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-981-10-6571-2_270