By Peter I. Hofgesang (auth.), I-Hsien Ting, Hui-Ju Wu (eds.)
Web mining purposes in E-commerce and E-services is a brand new learn course within the quarter of net mining. between the entire attainable functions in net study, e-commerce and e-services were pointed out as very important domain names for Web-mining concepts. Web-mining strategies additionally play an immense position in e-commerce and eservices, proving to be invaluable instruments for knowing how ecommerce and e-service sites and providers are used. This publication for this reason collects new advancements and prime quality researches for the readers of this publication to appreciate the subjects of internet mining purposes in e-commerce and e-services in addition to the state-of-the-arts during this sector. The chapters during this e-book contain internet utilization mining and person looking habit research, semantic net mining, internet functionality mining, net mining for clients’ desire knowing, internet mining for social community research and net mining for P2P services.
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These systems generally, use the content-based component to overcome the new item start up problem, if a new item is present then it can still be recommended regardless if it was seen and rated. The collaboration component overcomes the problem of over specialization as is the case with pure content based systems. 2 Content-Based Recommender Systems Content-based recommender systems have been very popular over the past decade. They have mainly been employed in textual domains. They have their roots in information retrieval and text mining.
In: ACSW Frontiers 2004: Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation, pp. 121–126. : Learning and revising user profiles: The identification of interesting web sites. : Learning concept hierarchies from text corpa using formal concept hierarchies. : Ontology based personalized search and browsing. : Foga: A fuzzy ontology generation framework for scholarly semantic web. : Recommender systems. : A multi-agent system for distributed ontology learning.
The main idea here is to extract context and make use of the context within the personalization process. The user context is extracted from using fuzzy ontology. In the fuzzy ontology framework the concept link relationships are assigned a value [0, 1] which determines the degree to which each concept is related to each other. One concept can be related with some degree and the same concept can be related with another concept another degree. The user preference model is a representation of concepts.