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

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 463)

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.

Keywords

User interaction Social influence and propagation User behavior analysis Preference prediction Social network 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of InformationBeijing Wuzi UniversityBeijingChina
  2. 2.Beijing Advanced Innovation Center for Future Internet TechnologyBeijingChina
  3. 3.School of LogisticsBeijing Wuzi UniversityBeijingChina

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