Abstract
Top-k and skyline techniques have been used to address preference based queries for effective service selection. However, they do not consider the dependencies between attributes in user preferences. In this paper, we focus on developing top-k indexing methods based on Conditional Preference Networks. We first determine whether the correlation among service attributes is clear and definite. After that, we employ dimensionality reduction to reduce the dimensionality of the service space. We then use top-k query to further improve the scalability. We conduct extensive experiment and compare with other competitive indexing mechanism to demonstrate the effectiveness of the proposed approach.
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