The Impact of Social Networks on Multi-Agent Recommender Systems
| dc.creator | Link, Hamilton | |
| dc.creator | Saia, Jared | |
| dc.creator | Lane, Terran | |
| dc.creator | LaViolette, Randall A. | |
| dc.date | 2005-11-02 | |
| dc.date.accessioned | 2026-07-07T06:49:32Z | |
| dc.date.available | 2026-07-07T06:49:32Z | |
| dc.description | 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. | |
| dc.description | 12 pages, 4 figures. Published in the Proceedings of the Workshop on Cooperative Multi-Agent Learning (ECML/PKDD '05). Resubmitted to fix citations and metadata | |
| dc.identifier | https://arxiv.org/abs/cs/0511011 | |
| dc.identifier | http://arxiv.org/abs/cs/0511011 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/104383 | |
| dc.subject | Machine Learning | |
| dc.subject | Computational Complexity | |
| dc.subject | Multiagent Systems | |
| dc.subject | I.2.6; I.2.11 | |
| dc.title | The Impact of Social Networks on Multi-Agent Recommender Systems | |
| dc.type | text |