ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
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The gradient descent procedure in [19] for user preference retrieval is based on linear additions of documentsjudged by the user. In contrast we design in this paper amultiplicative gradient descent search algorithm MG thatuses a multiplicative query expansion strategy to adaptivelyimprove the query vector. Our work generalizes the work in[19] 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 applicableto real-valued vector space. The algorithm MG boosts theusefulness of an index term exponentially, while the algorithm in [19] does so linearly. We report a working prototype of the Web search project MAGRADS (MultiplicativeAdaptive Gradient Descent Search) which is built upon algorithm MG, and its search performance analysis.