Visible to the public Research on an Agent-Based Intelligent Social Tagging Recommendation System

TitleResearch on an Agent-Based Intelligent Social Tagging Recommendation System
Publication TypeConference Paper
Year of Publication2017
AuthorsAn, S., Zhao, Z., Zhou, H.
Conference Name2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)
ISBN Number978-1-5386-3022-8
KeywordsAdaptation models, Agent technology, common preference group recommendation, common preference group recommendation strategy, Computer architecture, data mining, Data models, equilibrium selection, Filtering, Human Behavior, human factors, information retrieval, intelligent social tagging recommendation system, multi-agent systems, personalized recommendation, personalized recommendation strategy, pubcrawl, recommender systems, Resource management, Scalability, self-adaptive recommendation, self-adaptive recommendation strategy, Servers, Social Agents, social networking (online), social tagging recommendation, social tagging users, tagging, user interest mining
Abstract

With the repaid growth of social tagging users, it becomes very important for social tagging systems how the required resources are recommended to users rapidly and accurately. Firstly, the architecture of an agent-based intelligent social tagging system is constructed using agent technology. Secondly, the design and implementation of user interest mining, personalized recommendation and common preference group recommendation are presented. Finally, a self-adaptive recommendation strategy for social tagging and its implementation are proposed based on the analysis to the shortcoming of the personalized recommendation strategy and the common preference group recommendation strategy. The self-adaptive recommendation strategy achieves equilibrium selection between efficiency and accuracy, so that it solves the contradiction between efficiency and accuracy in the personalized recommendation model and the common preference recommendation model.

URLhttps://ieeexplore.ieee.org/document/8047575
DOI10.1109/IHMSC.2017.17
Citation Keyan_research_2017