Difference between revisions of "Proposal 2nd Draft Nitin Yandong Ming Yanbo"
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=== Author Topic Model === | === Author Topic Model === | ||
[[File:Author_topic.png]] | [[File:Author_topic.png]] | ||
| + | |||
| + | Author-Topic model describes such a generative process about how each document is generated: | ||
| + | |||
| + | For each document: | ||
| + | |||
| + | * Choose an author | ||
| + | * Choose a topic | ||
| + | * Choose a word | ||
| + | |||
| + | The result obtained includes the topic distribution per each author, and word distribution per each topic. One possible application suggested by this paper is to find related authors by computing KL-divergence of different author's topic distribution. | ||
=== Author-Recipient-Topic Model === | === Author-Recipient-Topic Model === | ||
[[File:Author_recipient_topic.png]] | [[File:Author_recipient_topic.png]] | ||
Revision as of 19:18, 15 February 2011
Contents
Modeling Academic Collaboration and Influence in scholarly literature
Team members
Nitin Agarwal
Yanbo Xu
The Problem
New research papers are growing rapidly, especially in computer science field, making it hard to follow. Instead of wasting time reading all the papers, we want our computers to answer following questions:
- Who to collaborate with?
- Which work to cite?
- Who to review this paper (for conference organizers)?
Essentially, we d like to capture the interactions and relationships between people. For academia, it s mainly about collaboration and citation. There are approaches about content Analysis and/or connectivity Analysis.
Application
Who to collaborate with?
- Given a professor's name and his/her research topic, we want the computer to list the most possible researchers for him/her to collaborate.
- This can be stated as
- In this way, we can recommend a faculty member for you to collaborate with.
Bold text
Related work
Author Topic Model
Author-Topic model describes such a generative process about how each document is generated:
For each document:
- Choose an author
- Choose a topic
- Choose a word
The result obtained includes the topic distribution per each author, and word distribution per each topic. One possible application suggested by this paper is to find related authors by computing KL-divergence of different author's topic distribution.
Author-Recipient-Topic Model
