Abstract
Inferring latent user preferences using both structured and unstructured data is an important social computing task. In this paper, we propose a user preference representation based on user activities embedded in unstructured data to better encode the homophily theory. The representation of an individual user is learned using a embedding based method to integrate latent user preferences in social media. The method has the ability to integrate a variety of user activities based cues from user comments, user social network (i.e; follower/followee connections) and user interested topics which are indicated by the topics a user has participated in. Experiments are conducted to evaluate the prediction of each user's favorite team as a part of user preferences in a dataset collected from the Hu-pu basketball discussion forum.1 Results clearly indicate that our proposed user representation outperforms other user representation baselines. Integrating user social network and user interested topics with user comments can improve the overall performance of user preference prediction.
Original language | English |
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Title of host publication | Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016 |
Publisher | IEEE |
Pages | 3913-3921 |
Number of pages | 9 |
ISBN (Electronic) | 9781467390040 |
DOIs | |
Publication status | Published - 1 Jan 2016 |
Event | 4th IEEE International Conference on Big Data, Big Data 2016 - Washington, United States Duration: 5 Dec 2016 → 8 Dec 2016 |
Conference
Conference | 4th IEEE International Conference on Big Data, Big Data 2016 |
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Country/Territory | United States |
City | Washington |
Period | 5/12/16 → 8/12/16 |
Keywords
- embedding
- social networks
- user activities
- user preferences
- user representation
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Hardware and Architecture