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A multiplicative gradient descent search algorithm for user preference retrieval and its application to Web search

The gradient descent procedure of Wong et al. (1988) for user preference retrieval is based on linear additions of documents judged by the user. In contrast we design in this paper a multiplicative gradient descent search algorithm MG that uses a multiplicative query expansion strategy to adaptively improve the query vector. Our work generalizes the work of Wong et al. in the following two aspects: various updating functions may be used in our algorithm; and multiplicative updating for a weight is dependent on the value of the corresponding index term, which is more realistic and applicable to real-valued vector space. The algorithm MG boosts the usefulness of an index term exponentially, while the algorithm of Wong et al. does so linearly. We report a working prototype of the Web search project MAGRADS (Multiplicative Adaptive Gradient Descent Search) which is built upon algorithm MG, and its search performance analysis.