TY - JOUR AU - McNally, Kevin AU - O'Mahony, Michael P. AU - Coyle, Maurice AU - Briggs, Peter AU - Smyth, Barry T1 - A Case Study of Collaboration and Reputation in Social Web Search JO - ACM Transactions on Intelligent Systems and Technology PY - 2011/october VL - 3 IS - 1 SP - 4:1 EP - 4:29 UR - http://doi.acm.org/10.1145/2036264.2036268 M3 - 10.1145/2036264.2036268 KW - collaborative KW - heystaks KW - reputation KW - search KW - social KW - web L1 - SN - N1 - N1 - AB - 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%. ER - TY - CONF AU - McNally, Kevin AU - O'Mahony, Michael P. AU - Smyth, Barry AU - Coyle, Maurice AU - Briggs, Peter A2 - T1 - Towards a reputation-based model of social web search T2 - Proceedings of the 15th international conference on Intelligent user interfaces PB - ACM CY - New York, NY, USA PY - 2010/ M2 - VL - IS - SP - 179 EP - 188 UR - http://doi.acm.org/10.1145/1719970.1719996 M3 - 10.1145/1719970.1719996 KW - collaborative KW - heystaks KW - reputation KW - search KW - social KW - web L1 - SN - 978-1-60558-515-4 N1 - N1 - AB - 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. ER - TY - CONF AU - Agichtein, Eugene AU - Castillo, Carlos AU - Donato, Debora AU - Gionis, Aristides AU - Mishne, Gilad A2 - T1 - Finding high-quality content in social media T2 - Proceedings of the international conference on Web search and web data mining PB - ACM CY - New York, NY, USA PY - 2008/ M2 - VL - IS - SP - 183 EP - 194 UR - http://doi.acm.org/10.1145/1341531.1341557 M3 - 10.1145/1341531.1341557 KW - answering KW - collaborative KW - media KW - quality KW - question KW - reputation KW - search KW - social KW - web L1 - SN - 978-1-59593-927-2 N1 - Finding high-quality content in social media N1 - AB - 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 ER - TY - JOUR AU - Chen, Wei AU - Zeng, Qingtian AU - Wenyin, Liu AU - Hao, Tianyong T1 - A user reputation model for a user-interactive question answering system JO - Concurrency and Computation: Practice and Experience PY - 2007/ VL - 19 IS - 15 SP - 2091 EP - 2103 UR - http://dx.doi.org/10.1002/cpe.1142 M3 - 10.1002/cpe.1142 KW - answering KW - collaborative KW - question KW - reputation KW - search KW - social KW - web L1 - SN - N1 - N1 - AB - 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. ER - TY - CONF AU - Jurczyk, Pawel AU - Agichtein, Eugene A2 - T1 - Discovering authorities in question answer communities by using link analysis T2 - Proceedings of the sixteenth ACM conference on Conference on information and knowledge management PB - ACM CY - New York, NY, USA PY - 2007/ M2 - VL - IS - SP - 919 EP - 922 UR - http://doi.acm.org/10.1145/1321440.1321575 M3 - 10.1145/1321440.1321575 KW - answering KW - collaborative KW - quality KW - question KW - reputation KW - search KW - social KW - web L1 - SN - 978-1-59593-803-9 N1 - N1 - AB - 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. ER - TY - CHAP AU - Yu, Bin AU - Singh, Munindar A2 - Klusch, Matthias A2 - Kerschberg, Larry T1 - A Social Mechanism of Reputation Management in Electronic Communities T2 - Cooperative Information Agents IV - The Future of Information Agents in Cyberspace PB - Springer CY - Berlin/Heidelberg PY - 2000/ VL - 1860 IS - SP - 355 EP - 393 UR - http://dx.doi.org/10.1007/978-3-540-45012-2_15 M3 - 10.1007/978-3-540-45012-2_15 KW - collaborative KW - community KW - management KW - network KW - quality KW - reputation KW - search KW - social KW - trust KW - web L1 - SN - 978-3-540-67703-1 N1 - N1 - AB - 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. ER -