ALRS: Agent-based Literature Recommendation System
Lijian He, Houkuan Huang, Wei Zhang, Kai Zhao · 2009
In the recommendation system, the agents cooperate by giving and taking recommendations so they can help each other to gain relevant information. For a researcher, literatures play an important role in his everyday work. Although some tools can help them get literatures, there are still some limitations as lacking cooperation in search, monotony literature sources and deficient personality. In this paper, we propose ALRS, an agent- based literature recommendation system, with which researchers can cooperate when searching and sharing the literatures. Agents in ALRS, which play both roles of a searcher and a recommender, mimic human interactions and enhance the source of literature. The well-chosen interaction protocol and decision method based on accumulated experiences make agents choose the right recommender to provide literatures. Our methodology in ALRS is introduced in this paper.