Multi-Agent Multi-User Modeling in I-Help

This paper describesthe user modeling approach applied in I-Help, a distributed multi-agent based collaborative environment for peer help. There is a multitude of user modeling information in I-Help, developed by the various software agents populating the environment. These ‘user model fragments’ have been created in a variety of specific contexts to help achieve various goals. They are inherently inconsistent with one another and reflect not only characteristics of the users, but also certain social relationships among them. The paper explores some of the implications of multi-agent user modeling in distributed environments.     

Why do we trust new technology? A study of initial trust formation with organizational information systems

Recent trust research in the information systems (IS) field has described trust as a primary predictor of technology usage and a fundamental construct for understanding user perceptions of technology. Initial trust formation is particularly relevant in an IS context, as users must overcome perceptions of risk and uncertainty before using a novel technology. With initial trust in a more complex, organizational information system, there are a number of external determinants, trusting bases, that may explain trust formation and provide organizations with the needed levers to form or change individuals’ initial trust in technology. In this study, a research model of initial trust formation is developed and includes trusting bases, trusting beliefs, trusting attitude and subjective norm, and trusting intentions. Eight trusting base factors are assessed including personality, cognitive, calculative, and both technology and organizational factors of the institutional base. The model is empirically tested with 443 subjects in the context of initial trust in a national identity system (NID). The proposed model was supported and the results indicate that subjective norm and the cognitive–reputation, calculative, and organizational situational normality base factors significantly influence initial trusting beliefs and other downstream trust constructs. Factors from some of the more commonly investigated bases, personality and technology institutional, did not significantly affect trusting beliefs. The findings have strategic implications for agencies implementing e-government systems and organizational information systems in general.     

Systematic Review: Trust-Building Factors and Implications for Conversational Agent Design

ABSTRACT

Off-the-shelf conversational agents are permeating people’s everyday lives. In these artificial intelligence devices, trust plays a key role in users’ initial adoption and successful utilization. Factors enhancing trust toward conversational agents include appearances, voice features, and communication styles. Synthesizing such work will be useful in designing evidence-based, trustworthy conversational agents appropriate for various contexts. We conducted a systematic review of the experimental studies that investigated the effect of conversational agents’ and users’ characteristics on trust. From a full-text review of 29 articles, we identified five agent design-themes affecting trust toward conversational agents: social intelligence of the agent, voice characteristics and communication style, look of the agent, non-verbal communication, and performance quality. We also found that participants’ demographic, personality, or use context moderate the effect of these themes. We discuss implications for designing trustworthy conversational agents and responsibilities around on stereotypes and social norm building through agent design.     

Multi-relational reinforcement for computing credibility of nodes

Nowadays, growing number of social networks are available on the internet, with which users can conveniently make friends, share information, and exchange ideas with each other. As the result, large amount of data are generated from activities of those users. Such data are regarded as valuable resources to support different mining tasks, such as predicting friends for a user, ranking users in terms of their influence on the social network, or identifying communities with common interests. Traditional algorithms for those tasks are often designed under the assumption that a user selects another user as his friend based on their common interests. As a matter of fact, users on a social network may not always develop their friends with common interest. For example, a user may randomly select other users as his friends just in order to attract more links reversely from them. Therefore, such links may not indicate his influence. In this paper, we study the user rank problem in terms of their ‘real’ influences. For this sake, common interest relationships among users are established besides their friend relationships. Then, the credible trust link from one node to another is on account of their similarities, which means the more similar the two users, the more credible their trust relation. So the credibility of a node is high if its trust inlinks are credible enough. In this work, we propose a framework that computes the credibility of nodes on a multi-relational network using reinforcement techniques. To the best of our knowledge, this is the first work to assess credibility exploited knowledge on multi-relational social networks. The experimental results on real data sets show that our framework is effective.     

融合用户动态标签和信任关系的协同过滤算法

针对协同过滤推荐算法中的冷启动以及数据稀疏问题,提出一种融合用户动态标签和用户信任关系的矩阵概率分解模型。该模型首先通过构建用户集、标签集和物品集三者间的动态联系,建立用户动态偏好矩阵;接着构建基于用户社会网络信息的用户信任关系矩阵,该信任关系矩阵使用用户信任反馈机制以实时更新用户间的信任值;最后提出融合用户动态标签和用户信任关系的矩阵概率分解模型,并在MovieLens与Jester_Joke_data数据集上进行仿真实验。实验结果表明,该算法在绝对误差均值、准确率与召回率方面获得了较好的效果,在一定程度上能有效提高了协同过滤推荐算法的性能。     

“She is just stupid”—Analyzing user–agent interactions in emotional game situations

A multiplayer dice game was realized which is played by two users and one embodied conversational agent. During the game, the players have to lie to each other to win the game and the longer the game commences the more probable it is that someone is lying, which creates highly emotional situations. We ran a number of evaluation studies with the system. The specific setting allows us to compare user–user interactions directly with user–agent interactions in the same game. So far, the users’ gaze behavior and the users’ verbal behavior towards one another and towards the agent have been analyzed. Gaze and verbal behavior towards the agent partly resembles patterns found in the literature for human–human interactions, partly the behavior deviates from these observations and could be interpreted as rude or impolite like continuous staring, insulting, or talking about the agent. For most of these seemingly abusive behaviors, a more thorough analysis reveals that they are either acceptable or present some interesting insights for improving the interaction design between users and embodied conversational agents.     

The affective outcomes of using influence tactics in embodied conversational agents

In this study, we highlight the theoretical underpinnings of human impression management tactics and link them to current research in embodied conversational agents. Specifically, we incorporated impression management behaviors into an embodied conversational agent in order to show that different influence strategies affect user perceptions, and how those perceptions might be moderated by user gender. We programmed the agent to use two human impression management techniques (ingratiation and self-promotion) and had the agent interact with 88 users. After the interaction, users reported their perceptions of the system’s power, trustworthiness, expertise, and attractiveness. The impression management techniques altered users’ perceptions and these perceptions were moderated by gender differences.     

Group affinity based social trust model for an intelligent movie recommender system

As many researchers have taken an interest in social networks with the development of the user-generated web, trust management and its application have come into the spotlight. User information that is extracted by behavior patterns and user profiles provides the essential relationship between individuals. In this paper, we propose an intelligent movie recommender system with a social trust model. The proposed system is based on a social network for analyzing social relationships between users and generated group affinity values with user profiles. In experiments, the performance of this system is evaluated with precision-recall and F-measures.     

I-TRUST: investigating trust between users and agents in a multi-agent portfolio management system

There is considerable research investigating trust among agents in multi-agent systems. However, the issue of trust between agents and users has rarely been reported in the literature. In this paper, we describe our experiences with I-TRUST, where trust is introduced as a relationship between clients and broker agents in terms of the amount of money clients are willing to give to these agents to invest on their behalf. The goals of broker agents are not only to maximize total revenue subject to clients’ risk preference, but also to reinforce trust with their clients. To achieve this, broker agents must first elicit user models both explicitly through questionnaires and implicitly through three games. Then based on the initial user models, a broker agent will learn to invest and later update user models when necessary. From the three experiments conducted in this study, we found that the controllability of a client towards the autonomous system plays a significant role for trust building.     

面向社会化电子商务的信任感知协同过滤推荐方法

为提高社会化电子商务推荐服务的精确度和有效性,综合考虑交易评价得分、交易次数、交易金额、直接信任、推荐信任等影响社会化电子商务用户信任关系的因素,设计了一种信任感知协同过滤推荐方法.该方法利用置信因子计算用户间的信任关系,采用余弦相关度法计算用户间的相似度,引入调和因子综合用户信任关系和用户相似度对商品预测评分的影响,以平均绝对误差(MAE)、评分覆盖率和用户覆盖率作为评价指标.实验结果表明,与标准协同过滤推荐方法、基于规范矩阵因式分解的推荐方法相比,信任感知协同过滤推荐方法将MAE降低到0.162,并将评分覆盖率和用户覆盖率分别提高到77%和80%,能够解决交易评价较少商品的推荐问题.     

A novel application recommendation method combining social relationship and trust relationship for future internet of things

Traditional collaborative filtering methods always utilize Cosine and Pearson methods to calculate the similarity of users. When the nearest neighbor doesn’t comment the predicted item, then the nearest neighbor has no influence on results, thus affecting the accuracy of collaborative filtering recommendation. And the traditional recommendation systems always have the problems of data sparsity, cold start and so on. In this paper, we consider social relationship and trust relationship, and put forward a novel application recommendation method that combines users’ social relationship and trust relationship. Specifically, we combine social relationship and user preference towards applications to calculate similarity score, we fuse the trust relationship based on familiarity and user reputation to calculate trust score. The final prediction score is calculated by fusing similar relationship and trust relationship properly. And the proposed method can effectively improve accuracy of recommendations.

     

Fully Embodied Conversational Avatars: Making Communicative Behaviors Autonomous

Although avatars may resemble communicative interface agents, they have for the most part not profited from recent research into autonomous embodied conversational systems. In particular, even though avatars function within conversational environments (for example, chat or games), and even though they often resemble humans (with a head, hands, and a body) they are incapable of representing the kinds of knowledge that humans have about how to use the body during communication. Humans, however, do make extensive use of the visual channel for interaction management where many subtle and even involuntary cues are read from stance, gaze, and gesture. We argue that the modeling and animation of such fundamental behavior is crucial for the credibility and effectiveness of the virtual interaction in chat. By treating the avatar as a communicative agent, we propose a method to automate the animation of important communicative behavior, deriving from work in conversation and discourse theory. BodyChat is a system that allows users to communicate via text while their avatars automatically animate attention, salutations, turn taking, back-channel feedback, and facial expression. An evaluation shows that users found an avatar with autonomous conversational behaviors to be more natural than avatars whose behaviors they controlled, and to increase the perceived expressiveness of the conversation. Interestingly, users also felt that avatars with autonomous communicative behaviors provided a greater sense of user control.     

A semantic and social-based collaborative recommendation of friends in social networks

The development of social media technologies has greatly enhanced social interactions. The proliferation of social platforms has generated massive amounts of data and a considerable number of persons join these platforms every day. Therefore, one of the current issues is to facilitate the search for the most appropriate friends for a given user. We focus in this article on the recommendation of users in social networks. We propose a novel approach which combines a user-based collaborative filtering (CF) algorithm with semantic and social recommendations. The semantic dimension suggests the close friends based on the calculation of the similarity between the active user and his friends. The social dimension is based on some social-behavior metrics such as friendship and credibility degree. The novelty of our approach concerns the modeling of the credibility of the user, through his/her trust and commitment in the social network. A social recommender system based on this approach is developed and experiments have been conducted using the Yelp social network. The evaluation results demonstrated that the proposed hybrid approach improves the accuracy of the recommendation compared with the user-based CF algorithm and solves the sparsity and cold start problems.     

基于用户及其行为社会属性的信任测度模型

信任测度是信任机制的核心和基础,现有的信任机制面临着恶意用户操纵信誉的安全威胁。基于用户及其行为社会属性的信任测度模型对传统的信任机制进行了扩充,引入用户及其行为所映射的本质特性即社会属性来描述和分析恶意用户及其行为的特征,在信任测度过程中增加信誉评审过程来修正对信任测度的攻击,从而保证了分布式环境中的信任测度的可信性。模拟实验表明,该信任测度模型能有效地应对恶意用户对信誉的操纵攻击。     

Promoting cooperation among strangers to access Internet services from an ad hoc network

We envision highly mobile users cooperating by sharing telecommunication connections to support a continuous messaging notification channel. Peer-to-peer sharing would enable a reduction of users’ telecommunication charges and devices’ battery consumption. Nevertheless, without a centralized trust authority, people lack the incentive to cooperate with a group of strangers. We present a new distributed trust framework and a credit system to solve this problem. Trust is evaluated based on a user’s own experience and information obtained from others. The credit system is built on top of the trust system to ensure that each user appropriately takes turns providing the proxy service for the group of peers. No centralized authority or long-term accountability is needed. Simulation results demonstrate that this framework is stable and efficient. Fairness is maintained among users and each user may benefit in proportion to its contribution to the group.     

How do varied populations interact with embodied conversational agents? Findings from inner-city adolescents and prisoners

Two studies were conducted to identify individual characteristics that predict behavioral responses to violence prevention interventions. These studies used embodied conversational agents (ECAs) to create hypothetical social situations (called virtual vignettes) to assess interpersonal competency skills. One study was of male inner-city African–American adolescents, and the second was of male prisoners in a state correctional system. In pre- and post-intervention sessions, participants interacted with an ECA that tried to entice them into making risky decisions. The virtual vignette sessions tested participants’ negotiation and conflict resolution skills. Results showed differing tendencies for participants to be engaged by the virtual vignettes. The vignettes were sufficiently realistic to elicit differences in behavior among the adolescents, but generally not for the prisoners. Prior acceptance, accessibility, and usability data suggest that most users readily accept ECAs as valid conversational partners. The evidence presented here suggests that the technology – or the setting in which the technology is used – is not by itself sufficient to actively engage users. The usefulness of virtual vignettes to adequately predict future behavior may be at least partially influenced by participant characteristics.     

Benevolence trust: a key determinant of user continuance use of online social networks

Online social networking (OSN) has attracted increased attention and growing membership in recent years. In this paper, we propose and test an extended and unified theory of acceptance and use of technology (UTAUT) model, including the additional areas of satisfaction, credibility trust, and benevolence trust, using an empirical survey of 676 OSN users to examine the influence of these factors. The results of regression analysis showed that the four key constructs of UTAUT (social influence, performance expectancy, effort expectancy, and facilitating conditions), as well as satisfaction, credibility trust, and benevolence trust, are all direct determinants of user continuance use of OSN. By comparing the coefficients of regression analysis, the relative importance of each determinant was also demonstrated. Results further indicate that benevolence trust has a much more significant effect on user continuance use of OSN than any other factor. A discussion is offered on the implications of these findings for OSN managers with regard to marketing and operations.     

基于用户信任和兴趣的概率矩阵分解推荐方法

传统协同过滤推荐算法存在数据稀疏性、冷启动、新用户等问题.随着社交网络和电子商务的迅猛发展,利用用户间的信任关系和用户兴趣提供个性化推荐成为研究的热点.本文提出一种结合用户信任和兴趣的概率矩阵分解（STUIPMF）推荐方法.该方法首先从用户评分角度挖掘用户间的隐性信任关系和潜在兴趣标签,然后利用概率矩阵分解模型对用户评分信息、用户信任关系、用户兴趣标签信息进行矩阵分解,进一步挖掘用户潜在特征,缓解数据稀疏性.在Epinions数据集上进行实验验证,结果表明,该方法能够在一定程度上提高推荐精度,缓解冷启动和新用户问题,同时具有较好的可扩展性.     

A novel recommendation approach based on users’ weighted trust relations and the rating similarities

With the growing popularity of open social networks, approaches incorporating social relationships into recommender systems are gaining momentum, especially matrix factorization-based ones. The experiments in previous literatures indicate that social information is very effective in improving the performance of traditional recommendation algorithms. However, most of existing social recommendation methods only take one kind of social relations—trust information into consideration, which is far from satisfactory. Furthermore, most of the existing trust networks are binary, which results in the equal treatment to different users who are trusted by the same user in these methods. In this paper, based on matrix factorization methods, we propose a new approach to make recommendation with social information. Its novelty can be summarized as follows: (1) it shows how to add different weights on the social trust relationships among users based on the trustee’s competence and trustworthiness; (2) it incorporates the similarity relationships among users as a complement into the social trust relationships to enhance the computation of user’s neighborhood; (3) it can balance the influence of these two kinds of relationships based on user’s individuality adaptively. Experiments on Epinions and Ciao datasets demonstrate that our approach outperforms the state-of-the-art algorithms in terms of mean absolute error and root mean square error, in particular for the users who rated a few items.     

结合信任机制和用户偏好的协同过滤推荐算法

仅凭相似度来定位邻居用户对传统协同过滤算法的性能有严重的负面影响。引入社会网络中的信任机制,从个体在社交圈中的主观信任和全局声誉角度出发建模。分别考虑用户交互、评分差和用户偏好调节生成直接信任度。利用声誉及专家信任优先模型聚合生成间接信任度,将两者动态加权形成用户之间的信任关系。用参数[η]协调信任和相似双属性,使用户关系更加紧密,有效地解决新用户和稀疏性问题。经实证,改良后的模型颇有成效。