Protecting Online Rating Systems from Unfair Ratings
Online rating systems have been widely adopted by online trading communities to ban “bad” service providers and prompt them to provide “good” services. However, the performance of the online rating systems is easily compromised by various unfair ratings, e.g. balloting, badmouthing, and complementar...
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| creator | Weng, Jianshu Miao, Chunyan Goh, Angela |
| description | Online rating systems have been widely adopted by online trading communities to ban “bad” service providers and prompt them to provide “good” services. However, the performance of the online rating systems is easily compromised by various unfair ratings, e.g. balloting, badmouthing, and complementary unfair ratings. How to mitigate the influence of the unfair ratings remains an important issue in online rating systems. In this paper, we propose a novel entropy-based method to measure the rating quality as well as to screen the unfair ratings. Experimental results show that the proposed method is both effective and practical in alleviating the influence of different types of unfair ratings. |
| doi_str_mv | 10.1007/11537878_6 |
| format | Conference Proceeding |
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However, the performance of the online rating systems is easily compromised by various unfair ratings, e.g. balloting, badmouthing, and complementary unfair ratings. How to mitigate the influence of the unfair ratings remains an important issue in online rating systems. In this paper, we propose a novel entropy-based method to measure the rating quality as well as to screen the unfair ratings. Experimental results show that the proposed method is both effective and practical in alleviating the influence of different types of unfair ratings.</description><subject>Applied sciences</subject><subject>Beta Distribution</subject><subject>Computer science; control theory; systems</subject><subject>Computer systems and distributed systems. User interface</subject><subject>Exact sciences and technology</subject><subject>Local Rating</subject><subject>Majority Opinion</subject><subject>Mean Square Error</subject><subject>Memory and file management (including protection and security)</subject><subject>Memory organisation. 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User interface</topic><topic>Exact sciences and technology</topic><topic>Local Rating</topic><topic>Majority Opinion</topic><topic>Mean Square Error</topic><topic>Memory and file management (including protection and security)</topic><topic>Memory organisation. Data processing</topic><topic>Rating System</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Weng, Jianshu</creatorcontrib><creatorcontrib>Miao, Chunyan</creatorcontrib><creatorcontrib>Goh, Angela</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Weng, Jianshu</au><au>Miao, Chunyan</au><au>Goh, Angela</au><au>Pernul, Günther</au><au>Katsikas, Sokratis</au><au>López, Javier</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Protecting Online Rating Systems from Unfair Ratings</atitle><btitle>Lecture notes in computer science</btitle><date>2005</date><risdate>2005</risdate><spage>50</spage><epage>59</epage><pages>50-59</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>3540282246</isbn><isbn>9783540282242</isbn><eisbn>9783540317968</eisbn><eisbn>3540317961</eisbn><abstract>Online rating systems have been widely adopted by online trading communities to ban “bad” service providers and prompt them to provide “good” services. However, the performance of the online rating systems is easily compromised by various unfair ratings, e.g. balloting, badmouthing, and complementary unfair ratings. How to mitigate the influence of the unfair ratings remains an important issue in online rating systems. In this paper, we propose a novel entropy-based method to measure the rating quality as well as to screen the unfair ratings. Experimental results show that the proposed method is both effective and practical in alleviating the influence of different types of unfair ratings.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11537878_6</doi><tpages>10</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Lecture notes in computer science, 2005, p.50-59 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_17136164 |
| source | Springer Books |
| subjects | Applied sciences Beta Distribution Computer science control theory systems Computer systems and distributed systems. User interface Exact sciences and technology Local Rating Majority Opinion Mean Square Error Memory and file management (including protection and security) Memory organisation. Data processing Rating System Software |
| title | Protecting Online Rating Systems from Unfair Ratings |
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