User- and Community-Adaptive Rewards Mechanism for Sustainable Online Community

Cheng, Ran; Vassileva, Julita

doi:10.1007/11527886_43

Cited by 37 publications

(69 citation statements)

References 6 publications

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“…The first incentive mechanism applied in Comtella (Bretzke and Vassileva 2003;Cheng and Vassileva 2005a) rewarded users with points for actions that were beneficial for the community (contributing new papers, downloading papers from others and making them available for sharing with others). These were actions that the user had full control of and did not reflect the opinion of other users of the user's actions.…”

Section: Challenge In Selecting What User Actions To Reward With Repumentioning

confidence: 99%

“…The results of the evaluation of this mechanism showed a significant but short-term increase of participation. There were attempts by some users to game the system, by performing unreasonably high numbers of the rewarded actions (Cheng and Vassileva 2005a). Since the quality of the contributions was not evaluated, the users' participation in the system deteriorated due to the overwhelming amount of low-quality contributions and the resulting cognitive overload (Jones and Rafaeli 1999).…”

Section: Challenge In Selecting What User Actions To Reward With Repumentioning

confidence: 99%

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Motivating participation in social computing applications: a user modeling perspective

Vassileva

2012

User Model User-Adap Inter

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185

112

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The explosive growth of Web-based social applications over the last 10 years has led people to engage in online communities for various purposes: to work, learn, play, share time and mementos with friends and family and engage in public action. Social Computing Applications (SCA) allow users to discuss various topics in online forums, share their thoughts in blogs, share photos, videos, bookmarks, and connect with friends through social networks. Yet, the design of successful social applications that attract and sustain active contribution by their users still remains more of an art than a science. My research over the last 10 years has been based on the hypothesis that it is possible to incorporate mechanisms and tools in the design of the social application that can motivate users to participate, and more generally, to change their behavior in a desirable way, which is beneficial for the community. Since different people are motivated by different things, it can be expected that personalizing the incentives and the way the rewards are presented to the individual, would be beneficial. Also since communities have different needs in different phases of their existence, it is necessary to model the changing needs of communities and adapt the incentive mechanisms accordingly, to attract the kind of contributions that are beneficial. Therefore User and Group (Community) Modeling is an important area in the design of incentive mechanisms. This paper presents an overview of different approaches to motivate users to participate. These approaches are based on various theories from the area of social psychology and behavioral economics and involve rewards mechanisms, reputation, open group user modeling, and social visualization. Future trends are outlined towards convergence with the areas of persuasive systems design, adaptive/personalized systems, and intelligent social learning environments.

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Section: Challenge In Selecting What User Actions To Reward With Repumentioning

confidence: 99%

Section: Challenge In Selecting What User Actions To Reward With Repumentioning

confidence: 99%

Motivating participation in social computing applications: a user modeling perspective

Vassileva

2012

User Model User-Adap Inter

Self Cite

185

112

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“…While collaborative filtering mechanisms are mostly used in collaborative Web recommender systems reviewed in Chapter 9 of this book [71], a few of systems used it for providing social navigation support. A straightforward example of navigation support based on community ratings is provided by collaborative resource gathering systems such as CoFIND [39] or COMTELLA [26], which were reviewed in section 8.3.2 above. A more elaborate example is shown by the CourseAgent system [42].…”

Section: Social Mechanismsmentioning

confidence: 99%

“…In this situation the "conceptually stable" ordering offered by link sorting can become an attractive solution. Good examples of this may be found among adaptive news systems reviewed in Chapter 18 of this book [3] and collaborative resource gathering systems such as CoFIND [39] or COMTELLA [26]. Adaptive news systems typically present links to recommended news articles in a single list or on several pages by category.…”

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confidence: 99%

Adaptive Navigation Support

Brusilovsky

Lecture Notes in Computer Science

126

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Abstract.Adaptive navigation support is a specific group of technologies that support user navigation in hyperspace, by adapting to the goals, preferences and knowledge of the individual user. These technologies, originally developed in the field of adaptive hypermedia, are becoming increasingly important in several adaptive Web applications, ranging from Web-based adaptive hypermedia to adaptive virtual reality. This chapter provides a brief introduction to adaptive navigation support, reviews major adaptive navigation support technologies and mechanisms, and illustrates these with a range of examples. IntroductionAdaptive hypermedia [9] is a research area at the crossroads of hypermedia and user modeling. Adaptive hypermedia systems (AHS) offer an alternative to the traditional "one-size-fits-all" hypermedia and Web systems by adapting to the goals, interests, and knowledge of individual users as they are represented in the individual user models. This chapter is focused on adaptive navigation support technologies originally developed in the field of adaptive hypermedia. By adaptively altering the appearance of links on every browsed page, using such methods as direct guidance, adaptive ordering, link hiding and removal, and adaptive link annotation, these technologies support personalized access to information. Over the last 10 years, adaptive navigation support technologies have been used in many adaptive Web systems in a range of application areas from e-learning to e-commerce. The evaluation of these technologies has demonstrated their ability to allow users to achieve their goals faster, reduce navigational overhead, and increase satisfaction [7; 18; 50; 52; 73].After a brief introduction to the history of adaptive navigation support, this chapter offers a state-of-the-art overview of adaptive navigation support. The overview is divided into two parts. The first part focuses on adaptation technologies and attempts to answer the question: What kind of adaptation effects may be useful to provide guidance to the users of Web hypermedia systems? The second part focuses on adaptation mechanisms and attempts to answer the question: How can these adaptation effects be produced? Both parts are illustrated with a range of examples. The last section discusses the prospects of extending adaptive navigation support beyond Web hypermedia. Adaptive Navigation Support: From Adaptive Hypermedia to the Adaptive WebResearch on adaptive navigation support in hypermedia can be traced back to the early 1990's. By that time, several research teams had recognized standard problems found in static hypertext within different application areas, and had begun to explore various ways to adapt the behavior of hypertext and hypermedia systems to individual users. A number of teams addressed problems related to navigation in hypermediasuch as the problem of inefficient navigation or the problem of being lost-which had been discovered when the field of hypertext reached relative maturity at the end of the 1980's [46]. Within a few year...

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“…Recent experimentation in learning networks have shown that incentive mechanisms like the adaptive introduction of extra (bonus) material based on contributions can increase both active and passive participation in learning networks [10]. Beside the individual activity adaptive rewarding mechanisms [3] could also take into account the current needs of the community and the style and quality of individual contributions in the past.…”

Section: Social Exchange Theory Reinforcement Mechanisms and Learninmentioning

confidence: 99%