The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform

Suzi Taylor - One of the best experts on this subject based on the ideXlab platform.

Carl Hallberg - One of the best experts on this subject based on the ideXlab platform.

Victoria Knight - One of the best experts on this subject based on the ideXlab platform.

Daniel Billsus - One of the best experts on this subject based on the ideXlab platform.

  • Adaptive Web Site Agents
    Autonomous Agents and Multi-Agent Systems, 2002
    Co-Authors: Michael J. Pazzani, Daniel Billsus
    Abstract:

    We discuss the design and evaluation of a class of agents that we call adaptive web site agents. The goal of such an agent is to help a user Find Additional Information at a particular web site, adapting its behavior in response to the actions of the individual user and the actions of other visitors to the web site. The agent recommends related documents to visitors and we show that these recommendations result in increased Information read at the site. It integrates and coordinates among different reasons for making recommendations including user preference for subject area, similarity between documents, frequency of citation, frequency of access, and patterns of access by visitors to the web site. We argue that this Information is best used not to change the structure or content of the web site but rather to change the behavior of an animated agent that assists the user.

  • Agents - Adaptive Web site agents
    Proceedings of the third annual conference on Autonomous Agents - AGENTS '99, 1999
    Co-Authors: Michael J. Pazzani, Daniel Billsus
    Abstract:

    We discuss the design and evaluation of a class of agents that we call adaptive web site agents. The goal of such an agent is to help a user Find Additional Information at a particular web site, adapting its behavior in response to the actions of the individual user and the actions of other visitors to the web site. The agent recommends related documents to visitors and we show that these recommendations result in increased Information read at the site. It integrates and coordinates among different reasons for making recommendations including user preference for subject area, similarity between documents, frequency of citation, frequency of access, and patterns of access by visitors to the web site. We argue that this Information is best used not to change the structure or content of the web site but rather to change the behavior of an animated agent that assists the user.

Michael J. Pazzani - One of the best experts on this subject based on the ideXlab platform.

  • Adaptive Web Site Agents
    Autonomous Agents and Multi-Agent Systems, 2002
    Co-Authors: Michael J. Pazzani, Daniel Billsus
    Abstract:

    We discuss the design and evaluation of a class of agents that we call adaptive web site agents. The goal of such an agent is to help a user Find Additional Information at a particular web site, adapting its behavior in response to the actions of the individual user and the actions of other visitors to the web site. The agent recommends related documents to visitors and we show that these recommendations result in increased Information read at the site. It integrates and coordinates among different reasons for making recommendations including user preference for subject area, similarity between documents, frequency of citation, frequency of access, and patterns of access by visitors to the web site. We argue that this Information is best used not to change the structure or content of the web site but rather to change the behavior of an animated agent that assists the user.

  • Agents - Adaptive Web site agents
    Proceedings of the third annual conference on Autonomous Agents - AGENTS '99, 1999
    Co-Authors: Michael J. Pazzani, Daniel Billsus
    Abstract:

    We discuss the design and evaluation of a class of agents that we call adaptive web site agents. The goal of such an agent is to help a user Find Additional Information at a particular web site, adapting its behavior in response to the actions of the individual user and the actions of other visitors to the web site. The agent recommends related documents to visitors and we show that these recommendations result in increased Information read at the site. It integrates and coordinates among different reasons for making recommendations including user preference for subject area, similarity between documents, frequency of citation, frequency of access, and patterns of access by visitors to the web site. We argue that this Information is best used not to change the structure or content of the web site but rather to change the behavior of an animated agent that assists the user.