Adaptive linear information retrieval models
SIGIR '87 Proceedings of the 10th annual international ACM SIGIR conference on Research and development in information retrieval
Linear structure in information retrieval
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Artificial Intelligence - Special issue on relevance
FEATURES: real-time adaptive feature and document learning for Web search
Journal of the American Society for Information Science and Technology
Some Formal Analysis of Roccio's Similarity-Based Relvance Feedback Algorithm
ISAAC '00 Proceedings of the 11th International Conference on Algorithms and Computation
WebSail: From On-line Learning to Web Search
WISE '00 Proceedings of the First International Conference on Web Information Systems Engineering (WISE'00)-Volume 1 - Volume 1
Using User Profiles in Intelligent Information Retrieval
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
A quadratic lower bound for rocchio's similarity-based relevance feedback algorithm
COCOON'05 Proceedings of the 11th annual international conference on Computing and Combinatorics
On the complexity of rocchio's similarity-based relevance feedback algorithm
ISAAC'05 Proceedings of the 16th international conference on Algorithms and Computation
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In contrast to the adoption of linear additive query updating techniques in existing popular algorithms for user preference retrieval, in this paper we design two types of algorithms, the multiplicative adaptive query expansion algorithm MA and the multiplicative adaptive gradient search algorithm MG, both of which use multiplicative query expansion strategies to adaptively improve the query vector. We prove that algorithm MA has a substantially better mistake bound than the Rocchio's and the Perceptron algorithms in learning a user preference relation determined by a linear classifier with a small number of non-zero coefficients over the real-valued vector space [0, 1]n.We also show that algorithm MG boosts the usefulness of an index term exponentially, while the gradient descent procedure does so linearly. Our work also generalize the algorithm Winnow in the following aspects: various updating functions may be used; 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; and finally, a number of documents which may or may not be counterexamples to the algorithm's current classification are allowed. Practical implementations of algorithms MA and MG have been underway in the next stage development of our intelligent web search tools.