Social-Aware Sequential Modeling of User Interests: A Deep Learning Approach
- 9 October 2018
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Knowledge and Data Engineering
- Vol. 31 (11), 2200-2212
- https://doi.org/10.1109/tkde.2018.2875006
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.Keywords
Funding Information
- National Natural Science Foundation of China (61772072)
- NSF (1525920, 1704662)
This publication has 26 references indexed in Scilit:
- Personalized Multimedia Recommendations for Cloud-Integrated Cyber-Physical SystemsIEEE Systems Journal, 2015
- Forgetting methods for incremental matrix factorization in recommender systemsPublished by Association for Computing Machinery (ACM) ,2015
- A survey on concept drift adaptationACM Computing Surveys, 2014
- Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine TranslationPublished by Association for Computational Linguistics (ACL) ,2014
- Breaking the habit: Measuring and predicting departures from routine in individual human mobilityPervasive and Mobile Computing, 2013
- Identifying Important Places in People’s Lives from Cellular Network DataLecture Notes in Computer Science, 2011
- NextPlace: A Spatio-temporal Prediction Framework for Pervasive SystemsLecture Notes in Computer Science, 2011
- Eigenbehaviors: identifying structure in routineBehavioral Ecology and Sociobiology, 2009
- The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem SolutionsInternational Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 1998
- Long Short-Term MemoryNeural Computation, 1997