<![CDATA[The quality of the website is quite important in generating customer satisfaction and loyalty. A website’s quality depends on several factors, features, and characteristics. Several computational methods are necessary to evaluate the quality of each factor and subsequently determine the overall quality of the entire website. Each factor does not contribute to the same level of quality required by the end users and thus requires a weighting system. Expert systems, which are either manually defined or learnt using artificial intelligence (AI), are to be modelled for assessing the quality of a factor/sub-factor or characteristics of a sub-factor. The quality of a website varies depending on the context. Context-based quality assessment of the websites is required. There is a need to generate example sets to assess the quality of websites and to establish relationships between web-related quality factors, subfactors, and characteristics. In this paper, a comprehensive framework is presented that caters to parametric structure building and mapping, parsers for computing characteristic values, context assessment, building expert systems, and learning models for assessing the quality of websites and weighing the factors that have specific significance on the quality of the website.]]>
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