Enhancing recommender systems by incorporating social information

Li-wei HUANG; Gui-sheng CHEN; Yu-cha LIU; De-yi LI

doi:10.1631/jzus.CIIP1303

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Enhancing recommender systems by incorporating social information

Computer & Automation | Updated：2022-12-09

- Enhancing recommender systems by incorporating social information
- Enhancing recommender systems by incorporating social information
- Frontiers of Information Technology & Electronic Engineering Vol. 14, Issue 9, Pages: 711-721(2013)
- Affiliations：
  
  1.Institute of Command Information System, PLA University of Science and Technology, Nanjing 210007, China
  2.Institute of Electronic System Engineering, Beijing 100039, China
- Author bio：
  
  [ "Li-wei HUANG, E-mail: huangliwei.1985@gmail.com" ]
  [ "De-yi LI, E-mail: lidy@cae.cn" ]
- Funds：
- DOI：10.1631/jzus.CIIP1303
  CLC： TP301.6
- Received：15 March 2013，
  
  Revised：2013-;8-;7，
  
  Published：2013-09
- Accepted：
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Li-wei HUANG, Gui-sheng CHEN, Yu-cha LIU, et al. Enhancing recommender systems by incorporating social information[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(9): 711-721.
DOI：

Li-wei HUANG, Gui-sheng CHEN, Yu-cha LIU, et al. Enhancing recommender systems by incorporating social information[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(9): 711-721. DOI： 10.1631/jzus.CIIP1303.

摘要

Abstract

Although recommendation techniques have achieved distinct developments over the decades

the data sparseness problem of the involved user-item matrix still seriously influences the recommendation quality. Most of the existing techniques for recommender systems cannot easily deal with users who have very few ratings. How to combine the increasing amount of different types of social information such as user generated content and social relationships to enhance the prediction precision of the recommender systems remains a huge challenge. In this paper

based on a factor graph model

we formalize the problem in a semi-supervised probabilistic model

which can incorporate different user information

user relationships

and user-item ratings for learning to predict the unknown ratings. We evaluate the method in two different genres of datasets

Douban and Last.fm. Experiments indicate that our method outperforms several state-of-the-art recommendation algorithms. Furthermore

a distributed learning algorithm is developed to scale up the approach to real large datasets.

关键词

Keywords

references

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