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- Publisher Website: 10.1088/0256-307X/30/11/118901
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Article: Long-term effects of recommendation on the evolution of online systems
| Title | Long-term effects of recommendation on the evolution of online systems |
|---|---|
| Authors | |
| Issue Date | 2013 |
| Citation | Chinese Physics Letters, 2013, v. 30, n. 11, article no. 118901 How to Cite? |
| Abstract | We employ a bipartite network to describe an online commercial system. Instead of investigating accuracy and diversity in each recommendation, we focus on studying the influence of recommendation on the evolution of the online bipartite network. The analysis is based on two benchmark datasets and several well-known recommendation algorithms. The structure properties investigated include item degree heterogeneity, clustering coefficient and degree correlation. This work highlights the importance of studying the effects and performance of recommendation in long-term evolution. © 2013 Chinese Physical Society and IOP Publishing Ltd. |
| Persistent Identifier | http://hdl.handle.net/10722/346583 |
| ISSN | 2023 Impact Factor: 3.5 2023 SCImago Journal Rankings: 0.815 |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Zhao, Dan Dan | - |
| dc.contributor.author | Zeng, An | - |
| dc.contributor.author | Shang, Ming Sheng | - |
| dc.contributor.author | Gao, Jian | - |
| dc.date.accessioned | 2024-09-17T04:11:50Z | - |
| dc.date.available | 2024-09-17T04:11:50Z | - |
| dc.date.issued | 2013 | - |
| dc.identifier.citation | Chinese Physics Letters, 2013, v. 30, n. 11, article no. 118901 | - |
| dc.identifier.issn | 0256-307X | - |
| dc.identifier.uri | http://hdl.handle.net/10722/346583 | - |
| dc.description.abstract | We employ a bipartite network to describe an online commercial system. Instead of investigating accuracy and diversity in each recommendation, we focus on studying the influence of recommendation on the evolution of the online bipartite network. The analysis is based on two benchmark datasets and several well-known recommendation algorithms. The structure properties investigated include item degree heterogeneity, clustering coefficient and degree correlation. This work highlights the importance of studying the effects and performance of recommendation in long-term evolution. © 2013 Chinese Physical Society and IOP Publishing Ltd. | - |
| dc.language | eng | - |
| dc.relation.ispartof | Chinese Physics Letters | - |
| dc.title | Long-term effects of recommendation on the evolution of online systems | - |
| dc.type | Article | - |
| dc.description.nature | link_to_subscribed_fulltext | - |
| dc.identifier.doi | 10.1088/0256-307X/30/11/118901 | - |
| dc.identifier.scopus | eid_2-s2.0-84890723223 | - |
| dc.identifier.volume | 30 | - |
| dc.identifier.issue | 11 | - |
| dc.identifier.spage | article no. 118901 | - |
| dc.identifier.epage | article no. 118901 | - |
| dc.identifier.eissn | 1741-3540 | - |