Mining user's Web searching skills through fuzzy cognitive state map

Meghabghab, George

doi:10.1109/nafips.2001.944291

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…A Fuzzy Cognitive Map (FCoM) is used in [23] for a causal modeling of the behavior of the user during her/his search on the Web. The learning of connections in the FCoM is made by neural learning of the weight matrix, using Hebbian law, when a change is detected.…”

Section: Web Personalizationmentioning

confidence: 99%

Web mining: a survey in the fuzzy framework

Arotăriței

Mitra

2004

Fuzzy Sets and Systems

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Section: Web Personalizationmentioning

confidence: 99%

Web mining: a survey in the fuzzy framework

Arotăriței

Mitra

2004

Fuzzy Sets and Systems

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“…The system uses fuzzy logic to deal with the uncertainty of document retrieval. Meghabghab 20 adopts a fuzzy cognitive map model to represent the users' behavioral patterns on the web.…”

Section: Introductionmentioning

confidence: 99%

Personal recommendation based on a user's understanding

Lee

2012

Comp Applic In Engineering

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Current web personalization mainly focuses on extracting web pages interesting to users.However, difficulty in understanding web contents often arises for some users mainly due to the lack of background knowledge on the contents. As a solution to this problem, this paper proposes a method of personalizing web search that produces search results suitable to the understanding level of a user. Fuzzy sets and membership functions are defined in order to keep adjusting the level of a user, which is updated based on the levels of web pages on which the user shows preferences. Extensive simulation experiments are conducted to tune the system parameters optimally and to show that the proposed system outperforms not only a general search engine but also a system that updates the understanding level of a user with the mean of the web page levels.

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“…Besides the applications in virtual worlds, FCM technique is applied to solve real-world problems. For example, FCM applications are used in intelligent intrusion detection system [6], the system to control the valves based on the tank height and the gravity of the liquid in the tank [7], the brain tumor characterization system [8], the web surveillance system [9], and so on. FCM shows high efficiency and accuracy in these systems.…”

Section: Introductionmentioning

confidence: 99%

Train Fuzzy Cognitive Maps by gradient residual algorithm

Zhang

Shen

Miao

2011

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)

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Fuzzy Cognitive Maps (FCM) is a popular technique for describing dynamic systems. A FCM for a dynamic system is a signed graph consisted of relevant concepts and causal relationships/weights between the concepts in the system. With suitable weights defined by experts in the related areas, the inference of the FCM can provide meaningful modeling of the system. Thus correctness of the weights is crucial to the success of a FCM system. Normally the weights are set by experts in the related areas. Considering the possible inefficiency and subjectivity of experts when judging the weights, it is an appealing idea to generate weights automatically according to the samples obtained through observation of the system. Some training algorithms were proposed. However, to our best knowledge, few learning algorithm has been reported to generate weight matrix based on sample sequences with continuous values. In this paper, we introduce a new learning algorithm to train the weights of FCM. In the proposed algorithm, the weights are updated by gradient descent on a squared Bellman residual, which is an accepted method in machine learning. The experiment results show that given sufficient training samples, the correct weights can be approximated by the algorithm. The algorithm proposes a new way for FCM research and applications.

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Mining user's Web searching skills through fuzzy cognitive state map

Cited by 6 publications

References 15 publications

Web mining: a survey in the fuzzy framework

Web mining: a survey in the fuzzy framework

Personal recommendation based on a user's understanding

Train Fuzzy Cognitive Maps by gradient residual algorithm

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