Understanding users and consequent personalization opportunities have become a major area of interest in gamification and UX research. Currently, personalization is mainly based on player typologies, which might give a partial picture of the plethora of user attributes. Addressing this challenge, in this study, we investigate the connections of the Hexad gamification user types, demographic factors, and gaming habits to understand how different user factors are related. Our results indicated significant but weak associations between user types and demographic factors and no significant association with gaming frequency-related factors. These results suggest that researchers and designers might need to consider more than the dominant factors to create personalized environments. We also provide exploratory suggestions on possible strategies to personalize gamification based on Hexad and other user factors. Our study contributes to the fields of user modeling and gamification, providing new insights into how different user characteristics are related while opening space for the conduction of new studies in the field.

There is a notable rise in websites and mobile apps that use manipulative (also known as "deceptive") designs or "dark patterns". Leveraging visual perception effects and cognitive biases or object manipulations, these designs influence user behavior in ways that may not be beneficial or can even be harmful for users. It is important to both warn and educate users about manipulative designs. While numerous studies have investigated warning designs across various domains, little attention has been given to exploring how to warn users about the presence of manipulative designs in applications. We conducted a user study with a three-level warning about the presence of manipulative designs on a simulated app page on the Google Play Store and explored the impact of different warning levels on user attention and decision-making. We also explored possibilities for personalization of warning levels based on the user’s personality (Big 5) characteristics. While our findings did not discover opportunities for personalization, they underscore the benefit of a multi-level warning design, and the pivotal role of visual elements in capturing attention, complemented by the contribution of textual explanations and more details on demand. We discuss the factors influencing users to install an app despite being informed about the presence of manipulative designs and demonstrate how app distribution platforms can embed warnings in the app information to prevent or mitigate the harms of manipulative designs.

E-learning has gained importance due to the Covid-19 pandemic safety measures that involved school closures. E-learning has many advantages and enables learning everywhere and at any time. However, research shows that the dropout rate for E-learning is 10-20% higher than for traditional learning. There are many potential reasons for the high dropout rate, such as technical problems, lack of support, poorly designed courses, etc. One of the important reasons is the lack of consideration and adaption to learner personality, emotion, mood, and motivation, which human teachers naturally do in face-to-face learning environments. That is why there is an interest in developing and implementing models and adapting to these human features in E-learning systems. This paper proposes a computational model based on Personality, Mood, Emotion, and Motivation that can be used in E-learning Environments. The model represents the reciprocal relations between Personality, Mood, Emotion, and Motivation.

The workshop aims to discuss open problems, challenges, and innovative research approaches in the area of persuasive and behavior change recommender systems, that is, recommender systems aimed at modifying people's habits and behavior. Some questions that motivate this workshop are: What kind of theory is more suitable to inform the design of behavior change recommender systems? What kind of personal data (e.g., coming from environmental sensors, wearable devices, etc.) should we use to design behavior change recommendations? How should we deliver them (i.e., what kind of communication channels and interfaces should we use)? What kind of strategies should we implement to design timely and contextualized recommendations? How can we support the user's motivation to adhere to the recommendations provided? How can we “persuade” users in the long term?

The half-day tutorial demonstrates how web-assembly techniques can be used to create sandboxed user models as digital twins within web- and mobile applications. Such models can learn from the user’s behaviour and personalise the application to the user, while ensuring transparency of the model, and ability for the user to experiment and control the personalization. The advantage of using webassembly techniques to implement digital twin user models is that the user model is a plugin, insulated from the application, and thus the protecting the privacy of the user model.

In recent decades, music recommender systems have become increasingly popular and have attracted a lot of research attention. While there has been significant progress in algorithm design to improve the quality of recommendations for listeners, there are new research challenges arising in large scale systems which have to consider the interests of both listeners and artists. To ensure a sustainable community of artists and a diversity of genres, artists, and songs, the recommender needs to ensure that new artists have a chance to be heard and rated. So, in addition to the objective of optimizing the recommendation to the preferences and enjoyment of the listener, a large scale MRS has a “fairness” objective to provide new artists (the protected group) with an opportunity to be heard. Previous research shows that using persuasive explanations can increase user acceptance of the recommended items. We propose to use persuasive justification messages for songs of new artists to influence user acceptance and satisfaction with these recommendations. The messages are designed to implement the six popular Cialdini persuasive strategies. We explore the effects of different persuasive messages on users with different Big-5 (OCEAN) personality types in an online study (n=205). The findings show that users with different personality traits are receptive to different persuasive messages and suggest how to personalize the persuasive justifications to amplify their effect for users with different personalities. These results can guide the development of personalized/ adaptive persuasive recommendation justifications for fair music recommender systems leading to a better user satisfaction and mitigating the “rich get richer” effect in large-scale music recommender systems, ensuring diversity of content and sustainability of the community.

We are proud to present the following three keynote speakers, who will share their expertise with the participants of ACM UMAP 2021, the 29th Conference on User Modeling, Adaptation and Personalization. In this article, you find the speakers’ biographies and the titles of their keynote talks.

No abstract available.

Engagement in learning activities is an important factor that affects student performance in education. According to research, student engagement involves the degree of passion, interest and attention that they exhibit in their educational environment. In the traditional learning system, educators encourage students to engage in their learning activities through various teaching strategies such as making them pay attention, take notes, ask questions and participate actively in the learning processes. Sometimes, educators call on a specific student to answer a question as a means of encouraging the student to participate in learning processes. Nowadays, engagement strategies for learning are changing, especially with the use of technology-enhanced learning systems (TELS) in education. As a result, improving the engagement level of students in online learning environments remains an open research question that needs to be explored. This research is part of a preliminary study on discovering ways of increasing student engagement in an online learning system through data-driven interventions. Student engagement in this research is determined using objective data (activity logs of a specific undergraduate course in a TELS). Activity log is unbiased data and a reflection of students' actual learning behaviours (uncontrolled). In this study, we mined the log of students' learning activities from a TELS used for an undergraduate course to explore differences between students' learning behaviours as they relate to their engagement level and academic performance (measured in terms of final grade points in a course). We employed supervised (Random Forest) and unsupervised (Clustering) machine learning approaches in exploring the relations. The approaches identified an interesting pattern on student engagement and show that engagement and assessment scores are good predictors of student academic performance. Assessment scores are measured with results of quizzes and assignments performed by the students in the TELS, while academic performance is measured with the final grade of the student in the course. The implications of our findings are discussed.

The success of persuasive systems in changing people's attitudes and behaviours has been established in various domains. Specifically, research has shown that personalized persuasive technology is more effective at achieving the desired goal than the one-size-fits-all approach. However, in the education domain, there are limited studies on the personalization of persuasive strategies to students. To advance persuasive technology research in this area, we investigated the susceptibility of undergraduate students (n = 243) to four persuasive strategies (Reward, Competition, Social Comparison and Social Learning) in order to provide a guideline for designing and personalizing persuasive systems in education. These four strategies were chosen because research on persuasion has established their effectiveness in changing behaviour and/or attitude. The results of our analysis reveal that students are more susceptible to Reward, followed by Competition and Social Comparison (both of which come in the second place) and Social Learning (the least persuasive). Moreover, there is no gender difference in the persuasiveness of the strategies. Therefore, in choosing persuasive strategies to motivate student's learning and success, among the strategies we investigated, Reward should be given priority, followed by Competition and Social Comparison, while Social Learning should be least favoured.

People typically eat what they shop for; if consumers shop for healthy foods, they will likely eat healthy foods. In order to influence healthier eating habits among consumers, it is important to identify the factors that influence them to shop for healthy foods. To contribute to ongoing research in this area, we explore the influence of commonly used e-commerce strategies: personality, persuasive strategies, social support, relative price, and perceived product quality on healthy shopping habits among e-commerce shoppers. Research has shown that personalizing these strategies makes them more effective in achieving the desired behavior change among users. Age and gender have been identified as factors that can be used for group-based personalization. We thus investigate the moderating effect of age and gender on the factors that influence healthy shopping habits in e-commerce shoppers. To achieve this, we carried out an online study of 244 e-commerce shoppers. Using partial least squares structural equation modeling (PLS-SEM), we developed a path model using the commonly used e-commerce factors: personality, persuasive strategies, social support, relative price, and perceived product quality. The result of our analysis suggests that social support, relative price and perceived product quality significantly influence healthy shopping habits in e-commerce shoppers. In addition, females are more influenced by social support to adopt healthy shopping habits compared to male e-shoppers. Furthermore, older shoppers are more influenced by social support to adopt healthy shopping habits, while the younger shoppers are more influenced by the relative price of products.

Behavior modeling has become a very important behavior change technique employed in most fitness apps. However, its effect as a persuasive strategy on users has not been well investigated. Consequently, we conducted an empirical study among 669 participants to uncover: (1) how the perceived persuasiveness of behavior model design influences three social cognitive theory (SCT) determinants of behavior: self-efficacy, self-regulation and outcome expectation; and (2) the moderating effect of gender-based personalization. We based our study on user evaluation of prototypes of behavior models performing push-up and squat exercise behaviors as a case study. Our results show that, overall, the perceived persuasiveness of behavior models significantly influences all of the three SCT factors. The effect of persuasiveness on self-regulation (β = 0.42, p < 0.001) and outcome expectation (β = 0.41, p < 0.001) is stronger than on self-efficacy (β = 0.13, p < 0.05). Moreover, the behavior model design has a stronger effect on females' self-efficacy and males' outcome expectation if personalized to their gender. We discuss the implication of our findings.

Personalizing persuasive technologies (PTs) is one of the hallmarks of a successful PT intervention. However, there is a lack of understanding of how Africans and North Americans differ or are similar in the susceptibility to persuasive strategies. To bridge this gap, we conducted a cross-cultural study among 284 subjects to investigate the moderating effect of culture on the susceptibility of users to Cialdini's principles of persuasion. Specifically, using Nigeria and Canada as a case study, we investigated how both groups vary in their levels of susceptibility to Authority, Commitment, Consensus, Liking, Reciprocity and Scarcity. The results of our analysis show that Nigerians are more susceptible to Authority and Scarcity than Canadians, while Canadians are more susceptible to Reciprocity, Liking and Consensus than Nigerians. However, both groups do not differ with respect to Commitment (the most persuasive strategy). Finally, we discussed our findings and mapped the most persuasive Cialdini's principles in each group to implementable persuasive strategies in the PT domain.

Active participation of users in Q&A social networks like Stack Overflow is key to the sustenance of the network. One way to encourage participation is to allow collaboration or cooperation between users in order to improve question and answer posts, and allow users to learn from one another. In order to implement strategies that encourage cooperation, it is important to understand what influences the users in the network to cooperate. In this extended abstract, we investigate the social support principles that influence cooperation in Stack Overflow. Using a sample size of 282 Stack Overflow users, we develop and test a global research model using partial least squares structural equation modelling (PLS-SEM). We further investigate any possible differences in the effect of these strategies between cultures, by testing two cultural subgroups; collectivist and individualist cultures. Our results show that social learning significantly influences cooperation in Stack Overflow at the global level. However, at the cultural subgroup level, recognition influences cooperation among collectivists, while social facilitation influences individualists to cooperate. These findings suggest possible design guidelines in the development of successful personalized Q&A social networking sites that encourage participation through cooperation.

There has been a continuous increase in the design and application of computer games for purposes other than entertainment in recent years. Serious games—games that motivate behavior and retain attention in serious contexts—can change the attitudes, behaviors, and habits of players. These games for change have been shown to motivate behavior change, persuade people, and promote learning using various persuasive strategies. However, persuasive strategies that motivate one player may demotivate another. In this article, we show the importance of tailoring games for change in the context of a game designed to improve healthy eating habits. We tailored a custom-designed game by adapting only the persuasive strategies employed; the game mechanics themselves did not vary. Tailoring the game design to players’ personality type improved the effectiveness of the games in promoting positive attitudes, intention to change behavior, and self-efficacy. Furthermore, we show that the benefits of tailoring the game intervention are not explained by the improved player experience, but directly by the choice of persuasive strategy employed. Designers and researchers of games for change can use our results to improve the efficacy of their game-based interventions.

Research has shown that in Q&A social networks, collaboration between respondents results in quality answers. Since good answers are required to keep any Q&A social network active, it is important to understand the characteristics of these collaborations and the collaborators. In this paper, we investigate how Stack Overflow promotes collaboration by allowing users to edit existing questions and answers in order to improve them. Using over 40,000 answer posts, our study reveals that collaboration in answer posts is not a function of achievement earned in terms of badges, as most edits associated with "best answer" rewards were posted by users who have not earned any answer badge. Our study further shows that posts that earned the "best answer" reward have more comments than those that did not. This study though, work in progress, can aid developers in implementing collaboration strategies in social networks that work.

Combining social network information with collaborative filtering recommendation algorithms has successfully reduced some of the drawbacks of collaborative filtering and increased the accuracy of recommendations. However, all approaches in the domain of research paper recommendation have used explicit social relations that users have initiated which has the problem of low recommendation coverage. We argued that the available data in social bookmarking Web sites such as CiteULike or Mendeley could be exploited to connect similar users using implicit social connections based on their bookmarking behavior. In this paper, we proposed three different implicit social networks-readership, co-readership, and tag-based and we compared the recommendation accuracy of several recommendation algorithms using data from the proposed social networks as input to the recommendation algorithms. Then, we tested which implicit social network provides the best recommendation accuracy. We found that, for the most part, the social recommender is the best algorithm and that the readership network with reciprocal social relations provides the best information source for recommendations but with low coverage. However, the co-readership network provide good recommendation accuracy and better user coverage of recommendation.

To persuade people to buy a product or service online, they must be visually convinced and attracted to use the sales website. Thus, there is need to understand how different user groups perceive various designs of websites for better adaptation. A lot of research has shown that users' judgment of the credibility of a website is critical to its success. However, in the mobile domain, little has been done empirically to 1) investigate users' credibility perception of a website; and 2) how it changes as the user interface (UI) design is systematically altered. This paper bridges this gap by carrying out sentiment and statistical analyses of users' perceptions of four systematically modified mobile websites among 285 subjects from North America, Africa and Asia. The results show that mobile website design affects the perception of its credibility, with 1) females being more critical and sensitive to UI changes than males; and 2) the grid-layout website design preferred to the list-layout website design by both genders. The study contributes to knowledge in three ways. First, it provides a concise model for understanding users' UI perceptions, expectations and gender differences. Second, it presents important findings that will enable a gender-based mobile website adaptation. Third, it provides a set of empirically backed guidelines for mobile web design.

In Q&A social networks, the few respondents that answer most of the questions are an asset to that network. Being able to predict the churn of these expert respondents will enable the owners of such network put things in place in order to keep them. In this paper, we predicted the churn of expert respondents in Stack Overflow. We identified experts based on the InDegree of the respondents and the value of the incentives earned by these experts from the questions they have answered in the past. Using four data mining techniques: logistic regression, neural networks, support vector machines and random forests, we predicted user churn and evaluated our results with four evaluation metrics: percentage correctly classified, area under receiver operating characteristic curve, precision and recall. Of the four data mining algorithms, random forests performed best with PCC of 76%, ROC area of 0.82, precision of 0.76 and recall of 0.77.

Digital behaviour interventions aim to encourage and support people to change their behaviour, for their own or communal benefits. Personalization plays an important role in this, as the most effective persuasive and motivational strategies are likely to depend on user characteristics. This tutorial covers the role of personalization in behaviour change technology, and methods and techniques to design personalized behaviour change technology.

The inherent uncertainties of open marketplaces motivate the design of reputation systems to facilitate buyers in finding honest feedback from other buyers (advisers). Defining the threshold for an acceptable level of honesty of advisers is very important, since inappropriately set thresholds would filter away possibly good advice, or the opposite – allow malicious buyers to badmouth good services. However, currently, there is no systematic approach for setting the honesty threshold. We propose a self-adaptive honesty threshold management mechanism based on PID feedback controller. Experimental results show that adaptively tuning the honesty threshold to the market performance enables honest buyers to obtain higher quality of services in comparison with static threshold values defined by intuition and used in previous work.

This paper describes and evaluates a method for presenting recommendations that will increase the efficiency of the social activity stream while preserving the users' accurate awareness of the activity within their own social networks. With the help of a content-based recommender system, the application displays the user's home timeline in Twitter as three visually distinct tiers by emphasizing more strongly those Tweets predicted to be more interesting. Pilot study participants reported that they were able to read the interesting Tweets while ignoring the others with relative ease and that the recommender accurately categorized their Tweets into three tiers.

In many research and business domains, there are efforts to develop systems that aggregate user data gathered by various data sources. This approach involves secondary sharing of user data and potentially benefits the user in terms of improved personalization and better experience. However, concerns regarding privacy arise when sharing user data with unknown third parties. These concerns can be alleviated at two stages: i) ensuring selective control of the applications to share user data with, and ii) monitoring and penalizing errant data consumers who violate the terms of their contractual agreement and potentially abuse user data. This paper addresses the second stage of data use contract enforcement. We propose a trust management mechanism for monitoring data consumers' compliance to the contractual agreements for which data was shared with them. The trust mechanism is based on user complaints about suspected privacy violations and is able to identify the data consumers who are responsible. The framework penalizes the data consumer found guilty of violating its data use agreement by decreasing its trust value. This makes the data consumer less likely to be selected to receive user data, and limits its participation in the user data marketplace, forcing it to pay a higher price for purchase of user data.

Abstract In this article, we propose a novel super‐agent‐based framework for reputation management and community formation in decentralized systems. We describe this framework in the context of Web service selection where agents with more capabilities act as super‐agents. These super‐agents serve as reputation managers to maintain reputation information of services and share the information with other consumer agents that have fewer capabilities than the super‐agents. In addition, super‐agents can maintain communities and build community‐based reputation for a service based on the opinions from all community members that have similar interests and judgement criteria as the super‐agents or the other community members. A practical reward mechanism is also introduced to create incentives for super‐agents to contribute their resources (to maintain reputation and form communities) and provide truthful reputation information. Experimental results obtained through simulation confirm that our approach achieves better effectiveness and scalability compared to the systems that do not use super‐agents and that do not form communities.

Persuasive games are an effective approach for motivating health behavior, and recent years have seen an increase in games designed for changing human behaviors or attitudes. However, these games are limited in two major ways: first, they are not based on theories of what motivates healthy behavior change. This makes it difficult to evaluate why a persuasive approach works. Second, most persuasive games treat players as a monolithic group. As an attempt to resolve these weaknesses, we conducted a large-scale survey of 642 gamers' eating habits and their associated determinants of healthy behavior to understand how health behavior relates to gamer type. We developed seven different models of healthy eating behavior for the gamer types identified by BrainHex. We then explored the differences between the models and created two approaches for effective persuasive game design based on our results. The first is a one-size-fits-all approach that will motivate the majority of the population, while not demotivating any players. The second is a personalized approach that will best motivate a particular type of gamer. Finally, to make our approaches actionable in persuasive game design, we map common game mechanics to the determinants of healthy behavior.

The Social Web is now a successful reality with its quickly growing number of users and applications. Also the Semantic Web, which started with the objective of describing Web resources in a machine-processable way, is now outgrowing the research labs and is being massively exploited in many websites, incorporating high-quality user-generated content and semantic annotations. The primary goal of this special section is to showcase some recent research at the intersection of the Social Web and the Semantic Web that explores the benefits that adaptation and personalization have to offer in the Web of the future, the so-called Social Adaptive Semantic Web. We have selected two articles out of fourteen submissions based on the quality of the articles and we present the main lessons learned from the overall analysis of these submissions.

Conflict emerging from collaboration in wiki can be helpful to achieve a better quality of collaborative learning. However, few studies have utilized conflict to support collaborative learning and wiki systems themselves have limitations. This paper proposes to provide visual feedback about conflict

PGP Web of Trust where users can sign digital signatures on public key certificates of other users has been successfully applied in securing emails and files transmitted over the Internet. However, its rigorous restrictions on utilizable trust relationships and acceptable signatures limit its performance. In this paper, we first make some modification and extension to PGP Web of Trust by relaxing those constraints. In addition, we propose a novel method to further expand trusted neighborhood of users by merging the signatures of the trusted neighbors and finding the similar users based on the merged signature set. Confirmed by the experiments carried out in different simulated real-life scenarios, our method applied to both the modified and extended PGP methods can improve their performance. With the expansion of trusted neighborhood, the performance of the original PGP Web of Trust is also improved considerably.

In this paper, we discuss some issues of extrinsic motivation in learning and argue that mechanism design can be applied on coursework grading to create incentives for student learning. We present an exploratory study using non-graded coursework in two fourth year university classes. Based on the study results, we discuss and suggest some important factors that influence students’ learning motives and that need to be taken into consideration for designing a proper coursework grading mechanism.

In this paper, we describe our goal of an effective course management system for assisting course managers to make informed decisions about what materials should be most appropriate to be presented to students (learners) and what learning strategies or methods should be used for the students. The system is supported by our design of a novel framework for user-driven data analytics in the cloud. Different modules of the framework will be illustrated in detail in the context of course management.

In this paper, we propose a novel community-based approach for web service selection where super-agents with more capabilities serve as community managers. They maintain communities and build community-based reputation for a service based on the opinions from all community members that have similar interests and judgement criteria. The community-based reputation is useful for consumer agents in selecting satisfactory services when they do not have much personal experience with the services. Experimental results show that our approach results in more effective service selection. A practical reward mechanism is also introduced to create incentives for super-agents to contribute their resources and provide truthful community-based reputation information, as strong support for our approach.

Browsing a blog archive is currently not well supported. Users cannot gain an overview of a blog easily, nor do they receive adequate support for finding potentially interesting entries in the blog. To overcome these problems, we developed a visualization tool that offers a new way to browse a blog archive. The main design principles of the tool are twofold. First, a blog should provide a rich overview to help users reason about the blog at a glance. Second, a blog should utilize social interaction history preserved in the archive to ease exploration and navigation. The tool was evaluated using a tool-specific questionnaire and the Questionnaire for User Interaction Satisfaction. Responses from the participants confirmed the utility of the design principles: the user satisfaction was high, supported by a low error rate in the given tasks. Qualitative feedback revealed that the decision to select which entry to read was multidimensional, involving factors such as the topic, the posting time, the length, and the number of comments on an entry. We discuss the implications of these findings for the design of navigational support for blogs, in particular to facilitate exploratory tasks.

In this paper we define some indicators that are useful to analyze the behavior of an individual within a learning community and hypothesize that there must exist some underlying factors relating to the user that explain the result obtained. We analyze the learner behaviors in the Comtella-D learning community and conclude that the actual data do not contradict our hypothesis.

The paper describes the evolution of the design of a motivational social visualization. The visualization shows the contributions of users to an online community to encourage social comparison and more participation. The newest design overcomes shortcomings in the previous two, by using more attractive appearance of the graphic elements in the visualization, better clustering algorithm and by giving up the largely unused in the previous design user customization options. The visualization integrates more information in one view, and uses an improved user clustering approach for representing graphically their different levels of contribution. A case study of the new design with a group of 32 students taking a class on Ethics and Computer Science is presented. The results show that the visualization had a significant effect on participation with respect to two activities (logging into the community and rating resources).

In this short paper, we describe our RSS recommender system, KeepUP. Too often recommender systems are seen as black box systems, resulting in general perplexity and dissatisfaction from users who are treated as passive, isolated consumers. Recent literature observes that recommendations rarely occur within such isolation and that there may be potential within more socially-orientated approaches. With KeepUP, we outline the design of a recommendation process that is based on an implicit social network where the relevancy and meaning of information can be negotiated not only with the recommender system but also with other users. Our overall goal is to support the formation and development of online communities of interest.

Most online communities, such as discussion forums, file-sharing communities, e-learning communities, and others, suffer from insufficient user participation in their initial phase of development. Therefore, it is important to provide incentives to encourage participation, until the community reaches a critical mass and “takes off”. However, too much participation, especially of low-quality can also be detrimental for the community, since it leads to information overload, which makes users leave the community. Therefore, to regulate the quality and the quantity of user contributions and ensure a sustainable level of user participation in the online community, it is important to adapt the rewards for particular forms of participation for individual users depending on their reputation and the current needs of the community. An incentive mechanism with these properties is proposed. The main idea is to measure and reward the desirable user activities and compute a user participation measure, then cluster the users based on their participation measure into different classes, which have different status in the community and enjoy special privileges. For each user, the reward for each type of activity is computed dynamically based on a model of community needs and an individual user model. The model of the community needs predicts what types of contributions (e.g. more new papers or more ratings) are most valuable at the current moment for the community. The individual model predicts the style of contributions of the user based on her past performance (whether the user tends to make high-quality contributions or not, whether she fairly rates the contributions of others). The adaptive rewards are displayed to the user at the beginning of each session and the user can decide what form of contribution to make considering the rewards that she will earn. The mechanism was evaluated in an online class resource-sharing system, Comtella. The results indicate that the mechanism successfully encourages stable and active user participation; it lowers the level of information overload and therefore enhances the sustainability of the community.

Decentralized peer-to-peer (P2P) networks can benefit from forming interest-based communities that can provide peers with information about the resources shared in the community and collectively computed rating of their quality as well as about the agents in the community and their reputation. We propose a mechanism for forming communities in a P2P system for sharing academic papers. The mechanism requires each agent to compute its trust in the agents with whom it interacts. A simulation shows that such communities can benefit peers.

Most of the research in the area of expert finding focuses on creating and maintaining centralized directories of experts' profiles, which users can search on demand. However, in a distributed multiagent‐based software environment, the autonomous agents are free to develop expert models or model fragments for their own purposes and from their viewpoints. Therefore, the focus of expert finding is shifting from the collection at one place as much data about a expert as possible to accessing on demand from various agents whatever user information is available at the moment and interpreting it for a particular purpose. This paper outlines purpose‐based expert modeling as an approach for finding an expert in a multiagent portfolio management system in which autonomous agents develop expert agent models independently and do not adhere to a common representation scheme. This approach aims to develop taxonomy of purposes that define a variety of context‐dependent user modeling processes, which are used by the users' personal agents to find appropriate expert agents to advise users on investing strategies.

Learning objects are reusable pieces of educational material intended to be strung together to form larger educational units such as activities, lessons, or whole courses. These materials are stored in learning object repositories which can be distributed in nature. We outline the issues associated with creating derivative works based on learning objects in a general manner, and discuss the support that exists within current metadata specifications.

It is important to enable peers to represent and update their trust in other peers in open networks for sharing files, and especially services. We propose a Bayesian network-based trust model and a method for building reputation based on recommendations in peer-to-peer networks. Since trust is multifaceted, peers need to develop differentiated trust in different aspects of other peers' capability. The peer's needs are different in different situations. Depending on the situation, a peer may need to consider its trust in a specific aspect of another peer's capability or in multiple aspects. Bayesian networks provide a flexible method to present differentiated trust and combine different aspects of trust. The evaluation of the model using a simulation shows that the system where peers communicate their experiences (recommendations) outperforms the system where peers do not share recommendations with each other and that a differentiated trust adds to the performance in terms of percentage of successful interactions.

We propose a Bayesian network-based trust model. Since trust is multifaceted, even in the same context, agents still need to develop differentiated trust in different aspects of other agents' behaviors. The agent's needs are different in different situations. Depending on the situation, an agent may need to consider its trust in a specific aspect of another agent's capability or in a combination of multiple aspects. Bayesian networks provide a flexible method to present differentiated trust and combine different aspects of trust. A Bayesian network-based trust model is presented for a file sharing peer-to-peer application.

We address long–term coalitions that are formed of both customer and vendor agents. We present a coalition formation mechanism designed at the agent level as a decision problem. The proposed mechanism is analyzed at both system and agent levels. Our results show that the coalition formation mechanism is beneficial for both the system—it reaches an equilibrium state—and for the agents—their gains highly increase over time.