Computational Web Intelligence: Intelligent Technology for by Tsau Young Lin, Abraham Kandel, Yan-Qing Zhang

By Tsau Young Lin, Abraham Kandel, Yan-Qing Zhang

This overview quantity introduces the unconventional clever net concept known as computational net intelligence (CWI) in response to computational intelligence (CI) and internet know-how (WT). It takes an in-depth examine hybrid net intelligence (HWI), that is in accordance with man made organic and computational intelligence with internet expertise and is used to construct hybrid clever internet structures that serve stressed and instant clients extra successfully. the elemental ideas of CWI and numerous e-applications of CWI and HWI are mentioned. For completeness, six significant CWI suggestions - fuzzy net intelligence, neural net intelligence, evolutionary internet intelligence, granular internet intelligence, tough net Intelligence and probabilistic internet intelligence - are defined. With the large capability for clever e-business purposes of CWI and HWI, those options signify the way forward for clever internet functions.

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This value always lies in the unit interval. An attribute, on the other hand, can be viewed as a variable that takes its value@) from its associated universe. In our framework the universe associated with an attribute corresponds to the subset of primitive assertions that is used to define it. Furthermore for a given object the value of an attribute depends upon the truth values of the associated primitives. Let us look at this. If Vj is a attribute we denote the variable corresponding to this attribute for a particular object d as Vj(d).

By a justification we shall mean a reason for believing a user may be interested in an object. These justifications can be obtained either from preferences directly expressed by user or induced using data about the users experiences. In the following we shall look at techniques for obtaining recommendations which make use of preferences directly expressed by a user. Here we consider the situation in which in addition to having a representation of the objects we assume the user has specified their preferences intentionally in a manner compatible with this representation.

Yager (1996) showed how to use linguistic quantifiers to generalize the logical quantification operation. , an) where Q is a linguistic quantifier and the aj are truth values. It was suggested that the truth value of this type of statement could be obtained with the aid of the OWA operator. This Recommender Systems Based on Representations 9 process involved first representing the quantifier Q as a fuzzy subset Q and then using Q to obtain an OWA weighting vector W which was used to perform an OWA aggregation of the ai.

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