@article{montaner2003taxonomy,
  abstract = {Recently, Artificial Intelligence techniques have proved useful inhelping users to handle the large amount of information on the Internet.The idea of personalized search engines, intelligent software agents,and recommender systems has been widely accepted among users who requireassistance in searching, sorting, classifying, filtering and sharingthis vast quantity of information. In this paper, we present astate-of-the-art taxonomy of intelligent recommender agents on theInternet. We have analyzed 37 different systems and their references andhave sorted them into a list of 8 basic dimensions. These dimensions arethen used to establish a taxonomy under which the systems analyzed areclassified. Finally, we conclude this paper with a cross-dimensionalanalysis with the aim of providing a starting point for researchers toconstruct their own recommender system.},
  author = {Montaner, Miquel and López, Beatriz and de la Rosa, Josep Lluís},
  doi = {10.1023/A:1022850703159},
  interhash = {3753781e80f4118f1dd77d7637be2f8b},
  intrahash = {f713e3f6acc112d9fbfd10216589d7db},
  issn = {0269-2821},
  journal = {Artificial Intelligence Review},
  language = {English},
  number = 4,
  pages = {285--330},
  publisher = {Kluwer Academic Publishers},
  title = {A Taxonomy of Recommender Agents on the Internet},
  url = {http://dx.doi.org/10.1023/A%3A1022850703159},
  volume = 19,
  year = 2003
}

@inproceedings{yu2003searching,
  abstract = {A referral system is a multiagent system whose member agents are capable of giving and following referrals. The specific cases of interest arise where each agent has a user. The agents cooperate by giving and taking referrals so each can better help its user locate relevant information. This use of referrals mimics human interactions and can potentially lead to greater effectiveness and efficiency than in single-agent systems.Existing approaches consider what referrals may be given and treat the referring process simply as path search in a static graph. By contrast, the present approach understands referrals as arising in and influencing dynamic social networks, where the agents act autonomously based on local knowledge. This paper studies strategies using which agents may search dynamic social networks. It evaluates the proposed approach empirically for a community of AI scientists (partially derived from bibliographic data). Further, it presents a prototype system that assists users in finding other users in practical social networks.},
  acmid = {860587},
  address = {New York, NY, USA},
  author = {Yu, Bin and Singh, Munindar P.},
  booktitle = {Proceedings of the second international joint conference on Autonomous agents and multiagent systems},
  doi = {10.1145/860575.860587},
  interhash = {1d5f1932e29ea02f82948d4efd12a0ad},
  intrahash = {c6b422948459e04a86e766055608e55e},
  isbn = {1-58113-683-8},
  location = {Melbourne, Australia},
  numpages = {8},
  pages = {65--72},
  publisher = {ACM},
  title = {Searching social networks},
  url = {http://doi.acm.org/10.1145/860575.860587},
  year = 2003
}