Titlebook: Intelligent Techniques for Web Personalization; IJCAI 2003 Workshop, Bamshad Mobasher,Sarabjot Singh Anand Conference proceedings 2005 Spri

諄諄教誨

Modeling Web Navigation: Methods and Challengesorking model must automate aspects of human perception, decision making and physical control. To successfully predict human behavior, these automated processes must be consistent with the cognitive and physical limitations of human users. Predicted behavior might include which links users select, wh

Interlocking

The Traits of the Personabletial information in an information-seeking interaction. The specific focus is on personalizing interactions at web sites. Using ideas from partial evaluation and explanation-based generalization, I present a modeling methodology for reasoning about personalization. This approach helps identify seven

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Case-Based Recommender Systems: A Unifying Viewe the CBR problem solving methodology in a number of ways. The goal of the proposed framework is to illustrate both the common features of the various CBR-RSs as well as the points were these systems take different solutions. The proposed framework was derived by the analysis of some systems and tec

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Collaborative Filtering Using Associative Neural Memoryve filtering with the study of associative memory, which is a neural network architecture that is significantly different from the dominant feedforward design. There are two types of CF systems – user-based and item-based, and we show that our CF system can have both interpretations. We further prov

Inertia

Scaling Down Candidate Sets Based on the Temporal Feature of Items for Improved Hybrid Recommendatioommender systems. However, scalability still remains an obstacle to applying recommender mechanism for large-scale web-based systems where thousands of items and transactions are readily available. To deal with this issue, data mining techniques have been applied to reduce the dimensions of candidat

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