Efficient Top-k Searching According to User Preferences Based on Fuzzy Functions with Usage of Tree-Oriented Data Structures
Abstract
In the last few years, research of top-k processing is in progress in various domains. The aim of our research is efficient top-k searching of the best k objects with more attributes according to more complex user preferences.
We focus on a multi-user solution, where data is common for all users. We use a model of user preferences based on fuzzy functions, where each user can express his/her preferences for each attribute by a fuzzy function and mutual relations between the attributes by an aggregation function.
In our research we focus on top-k searching in tree-oriented data structures and we developed various top-k algorithms, which support the model of user preferences and solve top-k problem efficiently, i.e. without searching all of objects stored in the tree-oriented data structure. In future research, we want to study various models of user preferences and the usage of relevance feedback in repeated user queries.