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Temporally Like-minded User Community Identification through Neural Embeddings
- Source :
- CIKM
- Publication Year :
- 2017
- Publisher :
- ACM, 2017.
-
Abstract
- We propose a neural embedding approach to identify temporally like-minded user communities, i.e., those communities of users who have similar temporal alignment in their topics of interest. Like-minded user communities in social networks are usually identified by either considering explicit structural connections between users (link analysis), users' topics of interest expressed in their posted contents (content analysis), or in tandem. In such communities, however, the users' rich temporal behavior towards topics of interest is overlooked. Only few recent research efforts consider the time dimension and define like-minded user communities as groups of users who share not only similar topical interests but also similar temporal behavior. Temporal like-minded user communities find application in areas such as recommender systems where relevant items are recommended to the users at the right time. In this paper, we tackle the problem of identifying temporally like-minded user communities by leveraging unsupervised feature learning (embeddings). Specifically, we learn a mapping from the user space to a low-dimensional vector space of features that incorporate both topics of interest and their temporal nature. We demonstrate the efficacy of our proposed approach on a Twitter dataset in the context of three applications: news recommendation, user prediction and community selection, where our work is able to outperform the state-of-the-art on important information retrieval metrics.
- Subjects :
- Information retrieval
Social network
business.industry
Computer science
Context (language use)
02 engineering and technology
Recommender system
Machine learning
computer.software_genre
Identification (information)
Content analysis
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
business
Social network analysis
computer
Feature learning
Link analysis
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
- Accession number :
- edsair.doi...........35624470d682da57757684e520080edf