Social-aware Sequential Modeling of User Interests: A Deep Learning Approach

Chi Harold Liu, Jie Xu, Jian Tang, Jon Crowcroft

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In this paper, we propose to leverage the emerging deep learning techniques for sequential modeling of user interests based on big social data, which takes into account influence of their social circles. First, we present a preliminary analysis for two popular big datasets from Yelp and Epinions. We show statistically sequential actions of all users and their friends, and discover both temporal autocorrelation and social influence on decision making, which motivates our design. Then, we present a novel hybrid deep learning model, Social-Aware Long Short-Term Memory (SA-LSTM), for predicting the types of item/PoIs that a user will likely buy/visit next, which features stacked LSTMs for sequential modeling and an autoencoder-based deep model for social influence modeling. Moreover, we show that SA-LSTM supports end-to-end training. We conducted extensive experiments for performance evaluation using the two real datasets from Yelp and Epinions. The experimental results show that (1) the proposed deep model significantly improves prediction accuracy compared to widely used baseline methods; (2) the proposed social influence model works effectively; and (3) going deep does help improve prediction accuracy but a not-so-deep deep structure leads to the best performance.

Original languageEnglish (US)
JournalIEEE Transactions on Knowledge and Data Engineering
DOIs
StateAccepted/In press - Jan 1 2018

Keywords

  • Autoencoder
  • Correlation
  • Data models
  • Deep Learning
  • Knowledge engineering
  • Machine learning
  • Predictive models
  • Recurrent Neural Network
  • Recurrent neural networks
  • Social network services
  • Social Networking
  • User Interest Modeling

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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