TAILIEUCHUNG - Multi-agents and learning: Implications for Webusage mining

Characterization of user activities is an important issue in the design and maintenance of websites. Server weblog files have abundant information about the user’s current interests. This information can be mined and analyzed therefore the administrators may be able to guide the users in their browsing activity so they may obtain relevant information in a shorter span of time to obtain user satisfaction. Web-based technology facilitates the creation of personally meaningful and socially useful knowledge through supportive interactions, communication and collaboration among educators, learners and information. This paper suggests a new methodology based on learning techniques for a Web-based Multiagent-based application to discover the hidden patterns in the user’s visited links. It presents a new approach that involves unsupervised, reinforcement learning, and cooperation between agents. It is utilized to discover patterns that represent the user’s profiles in a sample website into specific categories of materials using significance percentages. These profiles are used to make recommendations of interesting links and categories to the user. The experimental results of the approach showed successful user pattern recognition, and cooperative learning among agents to obtain user profiles. It indicates that combining different learning algorithms is capable of improving user satisfaction indicated by the percentage of precision, recall, the progressive category weight and F1-measure. | Journal of Advanced Research 2016 7 285-295 Cairo University Journal of Advanced Research ORIGINAL ARTICLE Multi-agents and learning Implications for Webusage mining CrossMark Hewayda . Lotfy a Soheir . Khamis a Maie M. Aboghazalah b a Computer Science Mathematics Department Faculty of Science AinShams University Cairo Egypt b Pure Math. Computer Science Menoufia University Menoufia Egypt ARTICLE INFO ABSTRACT Article history Received 17 January 2015 Received in revised form 21 April 2015 Accepted 25 June 2015 Available online 6 July 2015 Keywords Recommendation system Personalized web search Reinforcement learning Cooperative learning Unsupervised learning Characterization of user activities is an important issue in the design and maintenance of websites. Server weblog files have abundant information about the user s current interests. This information can be mined and analyzed therefore the administrators may be able to guide the users in their browsing activity so they may obtain relevant information in a shorter span of time to obtain user satisfaction. Web-based technology facilitates the creation of personally meaningful and socially useful knowledge through supportive interactions communication and collaboration among educators learners and information. This paper suggests a new methodology based on learning techniques for a Web-based Multiagent-based application to discover the hidden patterns in the user s visited links. It presents a new approach that involves unsupervised reinforcement learning and cooperation between agents. It is utilized to discover patterns that represent the user s profiles in a sample website into specific categories of materials using significance percentages. These profiles are used to make recommendations of interesting links and categories to the user. The experimental results of the approach showed successful user pattern recognition and cooperative learning among agents to obtain user profiles. It indicates that .

• Quản trị Web
• Recommendation system
• Personalized web search
• Reinforcement learning
• Cooperative learning
• Unsupervised learning
• Adaptive system
• Adaptive tourist recommendation system
• Traveler’s specifications
• Conceptual frameworks
• Tourist recommendation framework
• Knowledge management system quality
• Management system quality
• The usage continuity
• Recommendation intention
• Continuance intention
• IS success model
• STCR IPNS
• Ready reckoner
• Soil test based fertilizer recommendation
• Integrated plant nutrition system
• Vegetable cowpea
• Video recommendation system
• Complex topic learning
• Sustainable design approach
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• Computational intelligence
• Information retrieval
• Multicriteria decision making
• DRIS norms
• Nutrient contents
• Leaf diagnosis
• Nutritional status of mango
• Diagnosis and recommendation integrated system
• BMC Bioinformatics
• Drug repositioning
• Multiple data sources
• Multiple features
• Pareto dominance
• Collaborative filtering
• Recommendation systems
• Data mining
• Neighborhood based
• Online service providers
• Personalized music recommender system using user contents
• Music contents and preference ratings
• Music recommendation
• Rating sparsity
• User based
• IEEE International
• Multimedia Technologies
• Authoring multimedia
• learning material
• technical discussion
• Data warehouse
• OLAP query log
• Recommender system
• RSS feeds
• Online social researches
• Graph theory
• Personalized learning
• Learning path
• Compulsory requirements
• Complementary material during
• During chat discussions
• Web based learning
• Knowledge sharing
• Recommends information sources according