With the sheer growth of online user data, it becomes challenging to develop preference learning algorithms that are sufficiently flexible in modeling but also affordable in com...
Kai Yu, Shenghuo Zhu, John D. Lafferty, Yihong Gon...
Pseudo-relevance feedback (PRF) via query-expansion has been proven to be effective in many information retrieval (IR) tasks. In most existing work, the top-ranked documents from...
The selection of indexing terms for representing documents is a key decision that limits how effective subsequent retrieval can be. Often stemming algorithms are used to normaliz...
Recommender Systems, based on collaborative filtering (CF), aim to accurately predict user tastes, by minimising the mean error achieved on hidden test sets of user ratings, afte...
Web search is increasingly exploiting named entities like persons, places, businesses, addresses and dates. Entity ranking is also of current interest at INEX and TREC. Numerical ...