  A generalized ensemble model (gEnM) for document ranking is proposed in this
paper. The gEnM linearly combines basis document retrieval models and tries to
retrieve relevant documents at high positions. In order to obtain the optimal
linear combination of multiple document retrieval models or rankers, an
optimization program is formulated by directly maximizing the mean average
precision. Both supervised and unsupervised learning algorithms are presented
to solve this program. For the supervised scheme, two approaches are considered
based on the data setting, namely batch and online setting. In the batch
setting, we propose a revised Newton's algorithm, gEnM.BAT, by approximating
the derivative and Hessian matrix. In the online setting, we advocate a
stochastic gradient descent (SGD) based algorithm---gEnM.ON. As for the
unsupervised scheme, an unsupervised ensemble model (UnsEnM) by iteratively
co-learning from each constituent ranker is presented. Experimental study on
benchmark data sets verifies the effectiveness of the proposed algorithms.
Therefore, with appropriate algorithms, the gEnM is a viable option in diverse
practical information retrieval applications.
