A Multi-Agent Recommender System
نویسندگان
چکیده
The large amount of pages in Websites is a problem for users who waste time looking for the information they really want. Knowledge about users’ previous visits may provide patterns that allow the customization of the Website. This concept is known as Adaptive Website: a Website that adapts itself for the purpose of improving the user's experience. Some Web Mining algorithms have been proposed for adapting a Website. In this paper, a recommender system using agents with two different algorithms (associative rules and collaborative filtering) is described. Both algorithms are incremental and work with binary data. Results show that this multi-agent approach combining different algorithms is capable of improving user's satisfaction.
منابع مشابه
Modeling a Smart Hospital Information Architecture Based on Internet of Things and Recommender Agent
Introduction: Today, healthcare organizations worldwide are aware of the significance of technology and its impact on the quality of care. Hospitals are one of the most crucial systems in which the utilization of information is particularly important for several reasons. Using discrete-event simulation and developing a recommender agent, this study aimed to allocate IoT devices to patients in s...
متن کاملEvaluation of recommender systems: A multi-criteria decision making approach
The evaluation and selection of recommender systems is a difficult decision making process. This difficulty is partially due to the large diversity of published evaluation criteria in addition to lack of standardized methods of evaluation. As such, a systematic methodology is needed that explicitly considers multiple, possibly conflicting metrics and assists decision makers to evaluate and find...
متن کاملModeling a Smart Hospital Information Architecture Based on Internet of Things and Recommender Agent
Introduction: Today, healthcare organizations worldwide are aware of the significance of technology and its impact on the quality of care. Hospitals are one of the most crucial systems in which the utilization of information is particularly important for several reasons. Using discrete-event simulation and developing a recommender agent, this study aimed to allocate IoT devices to patients in s...
متن کاملA Multi-Agent Recommender System for Planning Meetings
In this work we address the problem of arranging meetings for several participants taking into consideration constraints for personal agendas and transportation schedules. We have implemented a multi-agent recommender system that solves the problem. Building such applications implies to consider two main issues: collecting information from different sources on the Internet, and solving the prob...
متن کاملA multi Agent System Based on Modified Shifting Bottleneck and Search Techniques for Job Shop Scheduling Problems
This paper presents a multi agent system for the job shop scheduling problems. The proposed system consists of initial scheduling agent, search agents, and schedule management agent. In initial scheduling agent, a modified Shifting Bottleneck is proposed. That is, an effective heuristic approach and can generate a good solution in a low computational effort. In search agents, a hybrid search ap...
متن کاملHow to Improve Multi-Agent Recommendations Using Data from Social Networks?
User profiles have an important role in multi-agent recommender systems. The information stored in them improves the system’s generated recommendations. Multi-agent recommender systems learn from previous recommendations to update users’ profiles and improve next recommendations according to the user feedback. However, when the user does not evaluate the recommendations the system may deliver p...
متن کامل