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- Publisher Website: 10.1016/j.physa.2017.01.055
- Scopus: eid_2-s2.0-85009876937
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Article: Evaluating user reputation in online rating systems via an iterative group-based ranking method
Title | Evaluating user reputation in online rating systems via an iterative group-based ranking method |
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Authors | |
Keywords | Iterative refinement Ranking method Rating systems Reputation evaluation Spamming attack |
Issue Date | 2017 |
Citation | Physica A: Statistical Mechanics and its Applications, 2017, v. 473, p. 546-560 How to Cite? |
Abstract | Reputation is a valuable asset in online social lives and it has drawn increased attention. Due to the existence of noisy ratings and spamming attacks, how to evaluate user reputation in online rating systems is especially significant. However, most of the previous ranking-based methods either follow a debatable assumption or have unsatisfied robustness. In this paper, we propose an iterative group-based ranking method by introducing an iterative reputation–allocation process into the original group-based ranking method. More specifically, the reputation of users is calculated based on the weighted sizes of the user rating groups after grouping all users by their rating similarities, and the high reputation users’ ratings have larger weights in dominating the corresponding user rating groups. The reputation of users and the user rating group sizes are iteratively updated until they become stable. Results on two real data sets with artificial spammers suggest that the proposed method has better performance than the state-of-the-art methods and its robustness is considerably improved comparing with the original group-based ranking method. Our work highlights the positive role of considering users’ grouping behaviors towards a better online user reputation evaluation. |
Persistent Identifier | http://hdl.handle.net/10722/346626 |
ISSN | 2023 Impact Factor: 2.8 2023 SCImago Journal Rankings: 0.661 |
DC Field | Value | Language |
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dc.contributor.author | Gao, Jian | - |
dc.contributor.author | Zhou, Tao | - |
dc.date.accessioned | 2024-09-17T04:12:10Z | - |
dc.date.available | 2024-09-17T04:12:10Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Physica A: Statistical Mechanics and its Applications, 2017, v. 473, p. 546-560 | - |
dc.identifier.issn | 0378-4371 | - |
dc.identifier.uri | http://hdl.handle.net/10722/346626 | - |
dc.description.abstract | Reputation is a valuable asset in online social lives and it has drawn increased attention. Due to the existence of noisy ratings and spamming attacks, how to evaluate user reputation in online rating systems is especially significant. However, most of the previous ranking-based methods either follow a debatable assumption or have unsatisfied robustness. In this paper, we propose an iterative group-based ranking method by introducing an iterative reputation–allocation process into the original group-based ranking method. More specifically, the reputation of users is calculated based on the weighted sizes of the user rating groups after grouping all users by their rating similarities, and the high reputation users’ ratings have larger weights in dominating the corresponding user rating groups. The reputation of users and the user rating group sizes are iteratively updated until they become stable. Results on two real data sets with artificial spammers suggest that the proposed method has better performance than the state-of-the-art methods and its robustness is considerably improved comparing with the original group-based ranking method. Our work highlights the positive role of considering users’ grouping behaviors towards a better online user reputation evaluation. | - |
dc.language | eng | - |
dc.relation.ispartof | Physica A: Statistical Mechanics and its Applications | - |
dc.subject | Iterative refinement | - |
dc.subject | Ranking method | - |
dc.subject | Rating systems | - |
dc.subject | Reputation evaluation | - |
dc.subject | Spamming attack | - |
dc.title | Evaluating user reputation in online rating systems via an iterative group-based ranking method | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.physa.2017.01.055 | - |
dc.identifier.scopus | eid_2-s2.0-85009876937 | - |
dc.identifier.volume | 473 | - |
dc.identifier.spage | 546 | - |
dc.identifier.epage | 560 | - |