Mars: Multiplicative Adaptive Refinement Web Search

Multiplicative Adaptive Refinement Web Search

This chapter reports the project MARS (Multiplicative Adaptive Refinement Search), which applies a new multiplicative adaptive algorithm for user preference retrieval to Web searches. The new algorithm uses a multiplicative query expansion strategy to adaptively improve and reformulate the query vector to learn users’ information preference. The algorithm has provable better performance than th...

MARS: Applying Multiplicative Adaptive User Preference Retrieval to Web Search

A Differential Evolution and Spatial Distribution based Local Search for Training Fuzzy Wavelet Neural Network

Abstract   Many parameter-tuning algorithms have been proposed for training Fuzzy Wavelet Neural Networks (FWNNs). Absence of appropriate structure, convergence to local optima and low speed in learning algorithms are deficiencies of FWNNs in previous studies. In this paper, a Memetic Algorithm (MA) is introduced to train FWNN for addressing aforementioned learning lacks. Differential Evolution...

Adaptive User Modeling for Query Expansion

Web users are often overwhelmed by the number of result pages retrieved by search engines. Query expansion is a common search strategy to narrow the search scope and to increase the relevance of the result lists. Existing query expansion systems proposed by search engines rely on techniques like term co-occurrence or synonymy which do not take into account the user’s search behavior. In this ar...

Adaptive Refinement of Search Patterns for Distributed Information Gathering

Information brokerage systems support distributed information gathering for data collections stored within the World Wide Web or for other on-line data repositories. These systems present a unified face of the underlying heterogeneity, both in terms of the access/search protocols and in terms of the data schemata. Typically, the user may input some values (e.g., interpreted as strings within a ...