In this paper, we present a model of a trust-based recommendation system on a social network. The idea of the model is that agents use their social network to reach information and their trust relationships to filter it. We investigate how the dynamics of trust among agents affect the performance of the system by comparing it to a frequencybased recommendation system. Furthermore, we identify the impact of network density, preference heterogeneity among agents, and knowledge sparseness to be crucial factors for the performance of the system. The system self-organises in a state with performance near to the optimum; the performance on the global level is an emergent property of the system, achieved without explicit coordination from the local interactions of agents.
The increasing diversity of consumers' demand, as documented by the debate on the long tail of the distribution of sales volume across products, represents a challenge for retail stores. Recommender systems offer a tool to cope with this challenge. The recent developments in information technology and ubiquitous computing makes it feasible to move recommender systems from the on-line commerce, where they are widely used, to retail stores. In this paper, we aim to bridge the management literature and the computer science literature by analysing a number of issues that arise when applying recommender systems to retail stores: these range from the format of the stores that would benefit most from recommender systems to the impact of coverage and control of recommender systems on customer loyalty and competition among retail stores.
Despite the fact that social networks are ubiquitous on the Internet, only few websites exploit the potential of combining user communities and online marketplaces. Not many platforms allow users to engage in a phenomenon called "group buying" — buyers joining groups, or coalitions, to bundle their purchasing power towards sellers. We argue that this may be due to a lack of face-to-face interaction on the Internet; often, users do not know which other users to trust, which makes them suspicious of engaging in online business, in particular if many unknown other parties are involved. This situation, however, can be alleviated by leveraging the social networks of users: based on who a user knows and is connected to, a trust metric — for example, the TrustWebRank metric developed by us — can be computed to assess who else may be considered trustworthy to that user. In this paper, we build a simple agent-based model of coalition formation among agents in the setting of group buying in an electronic marketplace. In this model, agents use their trust relationships in order to determine who to form coalitions with. We show that this leads agents to experience high utility and that agents are able to learn who is trustworthy and who is not, even when they have no initial knowledge about the trustworthiness of other agents. This work may provide the foundation for a real-world application of an online coalition formation platform for e-commerce built on a social networking platform such as Facebook.
We study the performance of various agent strategies in an artificial investment scenario. Agents are equipped with a budget, x(t), and at each time step invest a particular fraction, q(t), of their budget. The return on investment (RoI), r(t), is characterized by a periodic function with different types and levels of noise. Risk-avoiding agents choose their fraction q(t) proportional to the expected positive RoI, while risk-seeking agents always choose a maximum value q max if they predict the RoI to be positive ("everything on red"). In addition to these different strategies, agents have different capabilities to predict the future r(t), dependent on their internal complexity. Here, we compare "zero-intelligent" agents using technical analysis (such as moving least squares) with agents using reinforcement learning or genetic algorithms to predict r(t). The performance of agents is measured by their average budget growth after a certain number of time steps. We present results of extensive computer simulations, which show that, for our given artificial environment, (i) the risk-seeking strategy outperforms the risk-avoiding one, and (ii) the genetic algorithm was able to find this optimal strategy itself, and thus outperforms other prediction approaches considered.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.