Abstract
Searching for a suitable item on the web becomes a tedious and time-consuming task. The success of that process depends on how well items are described, as well as how well a system is able to mimic a human way of performing selection. This is especially important when the user identifies a variety of criteria with different levels of priority. One of interesting developments of web 2.0 is tagging. Tagging is a process of describing web resources of any type for the identification purposes preformed by ordinary users. All items are annotated by anyone who wants to provide descriptions or comments about items. Each item together with all tags describing it is called a tag-cloud. In the paper we propose a novel approach of converting information embedded in tag-clouds into fuzzy representations of tags. Those fuzzy representations allow us to determine the strength of tag-item connections, and identify a single resource that satisfies a specific tag to the highest degree. We use this idea to represent users’ feedback as tag-based fuzzy sets which we combine with a lexicographical-based multi-criteria decision-making mechanism in order to build the selection engine for a knowledge-based recommender system.












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The terms “item” and “resource” are interchangeable.
LibraryThing.com.
This procedure represents a kind of "soft" lexicographic approach rather then a “hard/binary” lexicographic method described earlier.
A process of determining the values of B and C should take into account a distribution of occurrences, and should represents user’s point of view in identifying what is treated as not small anymore (B), and already large (C). In general, many different approached can be applied here.
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Reformat, M.Z., Yager, R.R. Tag-based fuzzy sets for criteria evaluation in on-line selection processes. J Ambient Intell Human Comput 2, 35–51 (2011). https://doi.org/10.1007/s12652-010-0037-8
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DOI: https://doi.org/10.1007/s12652-010-0037-8