CALVIN: A Personalized Web-Search Agent based on Monitoring User Actions
نویسندگان
چکیده
In this paper we describe Calvin, an intelligent agent that learns user interests by monitoring user activities while he/she searches and browses the Web. The user pro le is created and maintained from a contentbased and event-based analysis of the visited pages using Inductive Logic Programming. The user submits queries which are expanded considering the information represented in her/his pro le. Once the expanded query is submitted to and answered by a search engine, the agent performs a relevance ranking of the results based on the user interests. After some experiments, Calvin has demonstrated to be capable of learning and adapting user interests without any explicit feedback from her/him.
منابع مشابه
Web pages ranking algorithm based on reinforcement learning and user feedback
The main challenge of a search engine is ranking web documents to provide the best response to a user`s query. Despite the huge number of the extracted results for user`s query, only a small number of the first results are examined by users; therefore, the insertion of the related results in the first ranks is of great importance. In this paper, a ranking algorithm based on the reinforcement le...
متن کاملRRLUFF: Ranking function based on Reinforcement Learning using User Feedback and Web Document Features
Principal aim of a search engine is to provide the sorted results according to user’s requirements. To achieve this aim, it employs ranking methods to rank the web documents based on their significance and relevance to user query. The novelty of this paper is to provide user feedback-based ranking algorithm using reinforcement learning. The proposed algorithm is called RRLUFF, in which the rank...
متن کاملPersonalized Search on the World Wide Web
With the exponential growth of the available information on the World Wide Web, a traditional search engine, even if based on sophisticated document indexing algorithms, has difficulty meeting efficiency and effectiveness performance demanded by users searching for relevant information. Users surfing the Web in search of resources to satisfy their information needs have less and less time and p...
متن کاملUn système de recherche d'information personnalisée basé sur la modélisation multidimensionnelle de l'utilisateur. (Personalized Information retrieval system based on multidimensional user modeling)
The web explosion has led Information Retrieval (IR) to be extended and web search engines emergence. The conventional IR methods, usually intended for simple textual searches, faced new documents types and rich and scalable contents. The user, facing these evolutions, asks more for IR systems search results quality. In this context, the personalization main objective is improving results retur...
متن کاملLearning and inferencing in user ontology for personalized Semantic Web search
User modeling is aimed at capturing the users’ interests in a working domain, which forms the basis of providing personalized information services. In this paper, we present an ontology based user model, called user ontology, for providing personalized information service in the Semantic Web. Different from the existing approaches that only use concepts and taxonomic relations for user modeling...
متن کامل