Wednesday, January 6th 2016
Title: Investigating performance and scalability issues for rank learning with regression tree ensembles
When ranking Web pages against user queries (and their associated context), there exist a large number of signals that can be leveraged to determine relevance. Such signals include the similarity between the user's query (/profile) and various parts of the document or related anchor-text, the recency of the content, spam scores, etc. Rank learning algorithms provide a coherent framework for determining the best way to combine these signals in order to maximise retrieval performance. As such, they have become a crucial component of current Information Retrieval infrastructure.
State-of-the-art rank-learning techniques discover non-linear combinations of features and are mostly based on ensembles of regression trees, using either bagged & randomised regressors (as in Random Forests) or boosted ensembles (as in Gradient-boosted methods). With an interest in both the performance and scalability of these algorithms, we investigate the importance of three different aspects: (i) the number of negative examples used to train the algorithm, (ii) the size of the subsample used to learn individual trees, and (iii) the type of objective function used to recursively partition the feature space.
Colloquium coordinators are Prof.dr. M. Aiello (e-mail :
Prof.dr. M. Biehl (e-mail:
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