A model of a trust-based recommendation system on a social network
By Frank Edward Walter, Stefano Battiston, Frank Schweitzer. Autonomous Agents and Multi-Agent Systems, 2006
441 citations recorded by the source corpus.
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 frequency-based 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.
Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/a5d6c2e513b759163058f49c91d13b671a879807
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Yang Guo
- Lin-Shan Lee
- Hung-yi Lee
- Matei Ripeanu
- Konstantin Beznosov
- Yong Liu
- Harald Steck
- Xiwang Yang
In Statistical and Nonlinear Physics
- Hung-yi Lee
- Matei Ripeanu
- Konstantin Beznosov
- Yong Liu
- Harald Steck
- Xiwang Yang
- Yazan Boshmaf
- Sophie Ahrens
Citations in the corpus
- The socialbot network: when bots socialize for fame and money (2011, methodology): “This, however, has alarming implications on software systems that use the social graph of an OSN to provide services such as limiting Sybil nodes in distributed systems [45] and modeling trust in sociallyinformed recommender systems [43].”
- Recommender Systems (2012, background): “A different approach was proposed by [250] where the authors use the information contained in social relationships between the users (which may stem from users’ family or friendship relations), the transitivity of trust (as in [251]), and the propagation of users’ queries in social networks.”
- A survey of collaborative filtering based social recommender systems (2014, methodology): “Various techniques have been proposed to incorporate trust into CF approaches [46, 47, 50, 51, 52, 53, 54, 55, 56, 57].”
- Recurrent neural network based language model personalization by social network crowdsourcing (2013, background): “Personalization, which is an indispensable aspect in several real world applications nowadays, has been studied in a wide range of research fields, such as personalized web search [1, 2] and recommendation systems [3, 4, 5, 6].”