@inproceedings{wagner2013forthcomingcustomer, address = {Istanbul}, author = {Wagner, Ralf and Falkenreck, Christine}, booktitle = {{P}roceedings of the 42nd {EMAC} {C}onference, Lost in Translation: Marketing in an Interconnected World}, interhash = {86338bcaf1759860e2f0c1ab424a1775}, intrahash = {aeadc125d7629137f2a85a5b471773e6}, title = {Do Customer Club Memberships really Enhance Reputation, Customer Satisfaction and Loyalty in B-to-B Relationships?}, year = {2013 forthcoming} } @article{jsang2007survey, abstract = {Trust and reputation systems represent a significant trend in decision support for Internet mediated service provision. The basic idea is to let parties rate each other, for example after the completion of a transaction, and use the aggregated ratings about a given party to derive a trust or reputation score, which can assist other parties in deciding whether or not to transact with that party in the future. A natural side effect is that it also provides an incentive for good behaviour, and therefore tends to have a positive effect on market quality. Reputation systems can be called collaborative sanctioning systems to reflect their collaborative nature, and are related to collaborative filtering systems. Reputation systems are already being used in successful commercial online applications. There is also a rapidly growing literature around trust and reputation systems, but unfortunately this activity is not very coherent. The purpose of this article is to give an overview of existing and proposed systems that can be used to derive measures of trust and reputation for Internet transactions, to analyse the current trends and developments in this area, and to propose a research agenda for trust and reputation systems.}, author = {Jøsang, Audun and Ismail, Roslan and Boyd, Colin}, doi = {10.1016/j.dss.2005.05.019}, interhash = {00a2017a002b72c88f47d5d4cae8da22}, intrahash = {d5a0c8c0ce75635005311da023d17b6c}, issn = {0167-9236}, journal = {Decision Support Systems}, note = {Emerging Issues in Collaborative Commerce}, number = 2, pages = {618 - 644}, title = {A survey of trust and reputation systems for online service provision}, url = {http://www.sciencedirect.com/science/article/pii/S0167923605000849}, volume = 43, year = 2007 } @article{chen2007reputation, abstract = {In this paper, we propose a user reputation model and apply it to a user-interactive question answering system. It combines the social network analysis approach and the user rating approach. Social network analysis is applied to analyze the impact of participant users' relations to their reputations. User rating is used to acquire direct judgment of a user's reputation based on other users' experiences with this user. Preliminary experiments show that the computed reputations based on our proposed reputation model can reflect the actual reputations of the simulated roles and therefore can fit in well with our user-interactive question answering system. Copyright © 2006 John Wiley & Sons, Ltd.}, author = {Chen, Wei and Zeng, Qingtian and Wenyin, Liu and Hao, Tianyong}, doi = {10.1002/cpe.1142}, interhash = {c304f655ee6ee183e07192b9fed0d618}, intrahash = {858df3646b706ce6308a12cbf1585d58}, issn = {1532-0634}, journal = {Concurrency and Computation: Practice and Experience}, number = 15, pages = {2091--2103}, publisher = {John Wiley & Sons, Ltd.}, title = {A user reputation model for a user-interactive question answering system}, url = {http://dx.doi.org/10.1002/cpe.1142}, volume = 19, year = 2007 } @incollection{yu2000social, abstract = {Trust is important wherever agents must interact. We consider the important case of interactions in electronic communities, where the agents assist and represent principal entities, such as people and businesses. We propose a social mechanism of reputation management, which aims at avoiding interaction with undesirable participants. Social mechanisms complement hard security techniques (such as passwords and digital certificates), which only guarantee that a party is authenticated and authorized, but do not ensure that it exercises its authorization in a way that is desirable to others. Social mechanisms are even more important when trusted third parties are not available. Our specific approach to reputation management leads to a decentralized society in which agents help each other weed out undesirable players.}, address = {Berlin/Heidelberg}, affiliation = {Department of Computer Science, North Carolina State University, Raleigh, NC 27695-7534, USA}, author = {Yu, Bin and Singh, Munindar}, booktitle = {Cooperative Information Agents IV - The Future of Information Agents in Cyberspace}, doi = {10.1007/978-3-540-45012-2_15}, editor = {Klusch, Matthias and Kerschberg, Larry}, interhash = {1065a4963600ef4f9b4c034d3bbd9a50}, intrahash = {337afcb67138b927b27a9687199e8568}, isbn = {978-3-540-67703-1}, keyword = {Computer Science}, pages = {355--393}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {A Social Mechanism of Reputation Management in Electronic Communities}, url = {http://dx.doi.org/10.1007/978-3-540-45012-2_15}, volume = 1860, year = 2000 } @inproceedings{jurczyk2007discovering, abstract = {Question-Answer portals such as Naver and Yahoo! Answers are quickly becoming rich sources of knowledge on many topics which are not well served by general web search engines. Unfortunately, the quality of the submitted answers is uneven, ranging from excellent detailed answers to snappy and insulting remarks or even advertisements for commercial content. Furthermore, user feedback for many topics is sparse, and can be insufficient to reliably identify good answers from the bad ones. Hence, estimating the authority of users is a crucial task for this emerging domain, with potential applications to answer ranking, spam detection, and incentive mechanism design. We present an analysis of the link structure of a general-purpose question answering community to discover authoritative users, and promising experimental results over a dataset of more than 3 million answers from a popular community QA site. We also describe structural differences between question topics that correlate with the success of link analysis for authority discovery.}, acmid = {1321575}, address = {New York, NY, USA}, author = {Jurczyk, Pawel and Agichtein, Eugene}, booktitle = {Proceedings of the sixteenth ACM conference on Conference on information and knowledge management}, doi = {10.1145/1321440.1321575}, interhash = {1c2953be3517384681b6ac831da2c766}, intrahash = {35394620d2654db8543d5da60f6f00dc}, isbn = {978-1-59593-803-9}, location = {Lisbon, Portugal}, numpages = {4}, pages = {919--922}, publisher = {ACM}, title = {Discovering authorities in question answer communities by using link analysis}, url = {http://doi.acm.org/10.1145/1321440.1321575}, year = 2007 } @inproceedings{agichtein2008finding, abstract = {The quality of user-generated content varies drastically from excellent to abuse and spam. As the availability of such content increases, the task of identifying high-quality content sites based on user contributions --social media sites -- becomes increasingly important. Social media in general exhibit a rich variety of information sources: in addition to the content itself, there is a wide array of non-content information available, such as links between items and explicit quality ratings from members of the community. In this paper we investigate methods for exploiting such community feedback to automatically identify high quality content. As a test case, we focus on Yahoo! Answers, a large community question/answering portal that is particularly rich in the amount and types of content and social interactions available in it. We introduce a general classification framework for combining the evidence from different sources of information, that can be tuned automatically for a given social media type and quality definition. In particular, for the community question/answering domain, we show that our system is able to separate high-quality items from the rest with an accuracy close to that of humans}, acmid = {1341557}, address = {New York, NY, USA}, author = {Agichtein, Eugene and Castillo, Carlos and Donato, Debora and Gionis, Aristides and Mishne, Gilad}, booktitle = {Proceedings of the international conference on Web search and web data mining}, doi = {10.1145/1341531.1341557}, interhash = {72c7bf5d1c983c47bfc3c6cc9084c26c}, intrahash = {29c5c74d95dce215a9692b94fc619839}, isbn = {978-1-59593-927-2}, location = {Palo Alto, California, USA}, numpages = {12}, pages = {183--194}, publisher = {ACM}, title = {Finding high-quality content in social media}, url = {http://doi.acm.org/10.1145/1341531.1341557}, year = 2008 } @article{mcnally2011study, abstract = {Although collaborative searching is not supported by mainstream search engines, recent research has highlighted the inherently collaborative nature of many Web search tasks. In this article, we describe HeyStaks, a collaborative Web search framework that is designed to complement mainstream search engines. At search time, HeyStaks learns from the search activities of other users and leverages this information to generate recommendations based on results that others have found relevant for similar searches. The key contribution of this article is to extend the HeyStaks social search model by considering the search expertise, or reputation, of HeyStaks users and using this information to enhance the result recommendation process. In particular, we propose a reputation model for HeyStaks users that utilise the implicit collaboration events that take place between users as recommendations are made and selected. We describe a live-user trial of HeyStaks that demonstrates the relevance of its core recommendations and the ability of the reputation model to further improve recommendation quality. Our findings indicate that incorporating reputation into the recommendation process further improves the relevance of HeyStaks recommendations by up to 40%.}, acmid = {2036268}, address = {New York, NY, USA}, articleno = {4}, author = {McNally, Kevin and O'Mahony, Michael P. and Coyle, Maurice and Briggs, Peter and Smyth, Barry}, doi = {10.1145/2036264.2036268}, interhash = {d2c8b459dba49425b1db33bd0d6fae47}, intrahash = {2544752b497c7f74b809e2d24145f14b}, issn = {2157-6904}, issue_date = {October 2011}, journal = {ACM Transactions on Intelligent Systems and Technology}, month = oct, number = 1, numpages = {29}, pages = {4:1--4:29}, publisher = {ACM}, title = {A Case Study of Collaboration and Reputation in Social Web Search}, url = {http://doi.acm.org/10.1145/2036264.2036268}, volume = 3, year = 2011 } @inproceedings{mcnally2010towards, abstract = {While web search tasks are often inherently collaborative in nature, many search engines do not explicitly support collaboration during search. In this paper, we describe HeyStaks (www.heystaks.com), a system that provides a novel approach to collaborative web search. Designed to work with mainstream search engines such as Google, HeyStaks supports searchers by harnessing the experiences of others as the basis for result recommendations. Moreover, a key contribution of our work is to propose a reputation system for HeyStaks to model the value of individual searchers from a result recommendation perspective. In particular, we propose an algorithm to calculate reputation directly from user search activity and we provide encouraging results for our approach based on a preliminary analysis of user activity and reputation scores across a sample of HeyStaks users.}, acmid = {1719996}, address = {New York, NY, USA}, author = {McNally, Kevin and O'Mahony, Michael P. and Smyth, Barry and Coyle, Maurice and Briggs, Peter}, booktitle = {Proceedings of the 15th international conference on Intelligent user interfaces}, doi = {10.1145/1719970.1719996}, interhash = {039d613f3fae6adab294e4b52f1ecb0e}, intrahash = {877ae3a66485776de88ba99951d9af2c}, isbn = {978-1-60558-515-4}, location = {Hong Kong, China}, numpages = {10}, pages = {179--188}, publisher = {ACM}, title = {Towards a reputation-based model of social web search}, url = {http://doi.acm.org/10.1145/1719970.1719996}, year = 2010 } @inproceedings{Dellarocas:2000:IOR:352871.352889, acmid = {352889}, address = {New York, NY, USA}, author = {Dellarocas, Chrysanthos}, booktitle = {Proceedings of the 2nd ACM conference on Electronic commerce}, doi = {10.1145/352871.352889}, interhash = {5c4f092708c065dec499691002d23b22}, intrahash = {a7bdb5b2f9f8ca80fb87fd8a23850f53}, isbn = {1-58113-272-7}, location = {Minneapolis, Minnesota, United States}, numpages = {8}, pages = {150--157}, publisher = {ACM}, series = {EC '00}, title = {Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior}, url = {http://doi.acm.org/10.1145/352871.352889}, year = 2000 } @inproceedings{Whitby04filteringout, author = {Whitby, Andrew and Jøsang, Audun and Indulska, Jadwiga}, interhash = {151548725ff496b07dc97c37f28f9f69}, intrahash = {8d26ba3da23a9ba454281a670bd39a79}, title = {Filtering Out Unfair Ratings in Bayesian Reputation Systems}, year = 2004 } @inproceedings{Sherchan:2006:FMR:1141277.1141722, acmid = {1141722}, address = {New York, NY, USA}, author = {Sherchan, Wanita and Loke, Seng W. and Krishnaswamy, Shonali}, booktitle = {Proceedings of the 2006 ACM symposium on Applied computing}, doi = {http://doi.acm.org/10.1145/1141277.1141722}, interhash = {6a847312028872dfb07d2472b4e88ca6}, intrahash = {afa80aad5c9222e20177793dfae5945a}, isbn = {1-59593-108-2}, location = {Dijon, France}, numpages = {7}, pages = {1886--1892}, publisher = {ACM}, series = {SAC '06}, title = {A fuzzy model for reasoning about reputation in web services}, url = {http://doi.acm.org/10.1145/1141277.1141722}, year = 2006 } @inproceedings{FaWaP09, address = {Maastricht}, author = {Falkenreck, Christine and Wagner, Ralf}, booktitle = {Building Bridges connects People. Proceedings of the 17th International Colloquium in Relationship Marketing}, editor = {Odekerken-Schr\"oder, Gaby}, interhash = {311b320af6b62e96ba51e230e8358ff3}, intrahash = {a9b6038e5d2bb8d2da712abfae39efc8}, organization = {17th International Colloquium in Relationship Marketing}, title = {{B}roadening the {V}iew on {C}ustomers` {P}ropensity to {L}eave a {R}elationship}, year = 2009 } @book{FaWaS208, address = {New Orleans}, author = {Falkenreck, Christine and Wagner, Ralf}, booktitle = {Vortrag auf der AMS Cultural Perspectives in Marketing Conference}, interhash = {8303378ca532d3812b1c858d30ced116}, intrahash = {dfefdeccc624820ad21fb7346d08189e}, title = {Modeling Culture's Impact on Reputation Transfer in Direct Marketing}, year = 2008 } @book{FaWaS108, address = {Brighton}, author = {Falkenreck, Christine and Wagner, Ralf}, booktitle = {EMAC}, interhash = {fe136621b1928181259e5c25060af5f1}, intrahash = {555b79f528fb3b626548f9be267da980}, title = {Antecedents of Company Reputation Transfer in B2B Markets: Empirical Evidence from Five Different Cultures}, year = 2008 } @book{WaFaS108, address = {Beijing}, author = {Wagner, Ralf and Falkenreck, Christine}, booktitle = {12th International Conference on Corporate Reputation, Brand, Identity and Competitiveness}, interhash = {730815b1eec02ae2c25781f29923910c}, intrahash = {a652b5e9148a94f5f592725c30b3fb60}, title = {Impact of Direct Marketing Activities on Company Reputation Transfer Success: Empirical Evidence from Five Different Cultures}, year = 2008 }