IEICE TRANSACTIONS on Information
Inferring User Consumption Preferences from Social Media
Summary :
Social Media has already become a new arena of our lives and involved different aspects of our social presence. Users' personal information and activities on social media presumably reveal their personal interests, which offer great opportunities for many e-commerce applications. In this paper, we propose a principled latent variable model to infer user consumption preferences at the category level (e.g. inferring what categories of products a user would like to buy). Our model naturally links users' published content and following relations on microblogs with their consumption behaviors on e-commerce websites. Experimental results show our model outperforms the state-of-the-art methods significantly in inferring a new user's consumption preference. Our model can also learn meaningful consumption-specific topics automatically.
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.3 pp.537-545
- Publication Date
- 2017/03/01
- Publicized
- 2016/12/09
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2016EDP7265
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
Authors
Yang LI
School of Computer Science and Technology, Harbin Institute of Technology
Jing JIANG
Singapore Management University
Ting LIU
School of Computer Science and Technology, Harbin Institute of Technology
Keyword
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Yang LI, Jing JIANG, Ting LIU, "Inferring User Consumption Preferences from Social Media" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 3, pp. 537-545, March 2017, doi: 10.1587/transinf.2016EDP7265.
Abstract: Social Media has already become a new arena of our lives and involved different aspects of our social presence. Users' personal information and activities on social media presumably reveal their personal interests, which offer great opportunities for many e-commerce applications. In this paper, we propose a principled latent variable model to infer user consumption preferences at the category level (e.g. inferring what categories of products a user would like to buy). Our model naturally links users' published content and following relations on microblogs with their consumption behaviors on e-commerce websites. Experimental results show our model outperforms the state-of-the-art methods significantly in inferring a new user's consumption preference. Our model can also learn meaningful consumption-specific topics automatically.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016EDP7265/_p
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@ARTICLE{e100-d_3_537,
author={Yang LI, Jing JIANG, Ting LIU, },
journal={IEICE TRANSACTIONS on Information},
title={Inferring User Consumption Preferences from Social Media},
year={2017},
volume={E100-D},
number={3},
pages={537-545},
abstract={Social Media has already become a new arena of our lives and involved different aspects of our social presence. Users' personal information and activities on social media presumably reveal their personal interests, which offer great opportunities for many e-commerce applications. In this paper, we propose a principled latent variable model to infer user consumption preferences at the category level (e.g. inferring what categories of products a user would like to buy). Our model naturally links users' published content and following relations on microblogs with their consumption behaviors on e-commerce websites. Experimental results show our model outperforms the state-of-the-art methods significantly in inferring a new user's consumption preference. Our model can also learn meaningful consumption-specific topics automatically.},
keywords={},
doi={10.1587/transinf.2016EDP7265},
ISSN={1745-1361},
month={March},}
Copy
TY - JOUR
TI - Inferring User Consumption Preferences from Social Media
T2 - IEICE TRANSACTIONS on Information
SP - 537
EP - 545
AU - Yang LI
AU - Jing JIANG
AU - Ting LIU
PY - 2017
DO - 10.1587/transinf.2016EDP7265
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2017
AB - Social Media has already become a new arena of our lives and involved different aspects of our social presence. Users' personal information and activities on social media presumably reveal their personal interests, which offer great opportunities for many e-commerce applications. In this paper, we propose a principled latent variable model to infer user consumption preferences at the category level (e.g. inferring what categories of products a user would like to buy). Our model naturally links users' published content and following relations on microblogs with their consumption behaviors on e-commerce websites. Experimental results show our model outperforms the state-of-the-art methods significantly in inferring a new user's consumption preference. Our model can also learn meaningful consumption-specific topics automatically.
ER -
English
