Abstract
Content analysis and citation analysis are two common methods in recommending system. Compared with content analysis, citation analysis can discover more implicitly related papers. However, the citation-based methods may introduce more noise in citation graph and cause topic drift. Some work combine content with citation to improve similarity measurement. The problem is that the two features are not used to reinforce each other to get better result. To solve the problem, we propose a new algorithm, Topic Sensitive Similarity Propagation (TSSP), to effectively integrate content similarity into similarity propagation. TSSP has two parts: citation context based propagation and iterative reinforcement. First, citation contexts provide clues for which papers are topic related and filter out less relevant citations. Second, iteratively integrating content and citation similarity enable them to reinforce each other during the propagation. We also expand the basic idea of TSSP using a weighted content similarity measurement and generalize the whole algorithm to a multi-features based method. The experimental results of a user study show the expanded TSSP outperforms other algorithms in most cases. © 2006 - IOS Press and the authors. All rights reserved.
| Original language | English |
|---|---|
| Pages (from-to) | 271-287 |
| Journal | Web Intelligence and Agent Systems |
| Volume | 4 |
| Issue number | 3 |
| Publication status | Published - 2006 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Research Keywords
- Iterative reinforcement
- Multi-features
- Similarity propagation