User preference search method
A user and indexing technology, applied in the computer field, can solve the problem of high space complexity and achieve the effect of efficient retrieval service
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[0033] The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.
[0034] Previous indexing methods are based on an assumption: each dimension is equally important. For CP-net data, this assumption can be weakened. If there is no such assumption, the design of the indexing method needs to consider the correlation between the dimensions involved in the user query.
[0035]If the correlation between dimensions is determined to be unchanged for all user queries, we can use the method of dimensionality reduction to reduce the associated multi-dimensionality to one dimension. First, the multi-dimensional CP-net (user preference model) data is reduced to one-dimensional data using the Hilbert Curve (Hilbert curve) dimensionality reduction method, and then one-dimensional bitmap indexing (bitmap indexing) is built on the one-dimensional data or B+tree indexing (B+tree index). Suppose there are three attributes X1, X2, X3...
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