The Impact of Social Networks on Multi-Agent Recommender Systems
<|reference_start|>The Impact of Social Networks on Multi-Agent Recommender Systems: Awerbuch et al.'s approach to distributed recommender systems (DRSs) is to have agents sample products at random while randomly querying one another for the best item they have found; we improve upon this by adding a communication network. Agents can only communicate with their immediate neighbors in the network, but neighboring agents may or may not represent users with common interests. We define two network structures: in the ``mailing-list model,'' agents representing similar users form cliques, while in the ``word-of-mouth model'' the agents are distributed randomly in a scale-free network (SFN). In both models, agents tell their neighbors about satisfactory products as they are found. In the word-of-mouth model, knowledge of items propagates only through interested agents, and the SFN parameters affect the system's performance. We include a summary of our new results on the character and parameters of random subgraphs of SFNs, in particular SFNs with power-law degree distributions down to minimum degree 1. These networks are not as resilient as Cohen et al. originally suggested. In the case of the widely-cited ``Internet resilience'' result, high failure rates actually lead to the orphaning of half of the surviving nodes after 60% of the network has failed and the complete disintegration of the network at 90%. We show that given an appropriate network, the communication network reduces the number of sampled items, the number of messages sent, and the amount of ``spam.'' We conclude that in many cases DRSs will be useful for sharing information in a multi-agent learning system.<|reference_end|>
@article{link2005the,
    title={The Impact of Social Networks on Multi-Agent Recommender Systems},
    author={Hamilton Link and Jared Saia and Terran Lane and Randall A. LaViolette},
    journal={arXiv preprint arXiv:cs/0511011},
    year={2005},
    archivePrefix={arXiv},
    eprint={cs/0511011},
    primaryClass={cs.LG cs.CC cs.MA}
}