As the field of machine learning (ML) has matured, it has reached out to several related fields, both for challenging applications and problems (e.g., robotics), and for new techniques and methods (e.g., statistics and databases). One especially interesting source of both problems and methods for ML is the field of Information Retrieval (IR). IR has witnessed a boom in activity and attention in the 1990's, fueled in large part on the commercial side by web-based search engines, and in the scientific side by largescale, repeatable evaluations on common tasks, as illustrated by the TREC, MUC, TDT, and SUMMAC conferences. One important trend in information retrieval has been toward exploration of new tasks. Most early IR research focused on document retrieval for same-language ad-hoc queries. More recent work has explored cross-language information retrieval, in which the documents retrieved are written in a different language than the users' query; automated text categorization; text summarization; fact extraction; topic tracking and detection; and multi-media IR. As part of this trend, IR has necessarily reached out to related communities, such as the natural language processing community, the speech recognition community, and the machine learning (ML) community. ML techniques have been applied to several IR problems, including text categorization, topic tracking and detection, and trainable cross-language IR. Although IR and ML have both benefited from intellectual cross-pollination in recent years, the ML and IR scientific communities still largely move in different orbits. Different terminology is often used; existing results are sometimes inadvertently re-invented; different evaluation metrics are used; and the two communities often focus on different aspects many problems. For instance, consider the so-called "curse of dimensionality." In ML, learning in high dimensions is usually considered intractable, while IR systems typically deal with dimensions proportional to the size of vocabulary (e.g., 10,000 or more), and with instance spaces that populate these dimensions extremely sparsely. One might think, then, that existing ML techniques would simply be inapplicable to these IR problems. However, in practice, the same ML methods with intractable theoretical worst-case performance on high dimensional problems often work well (with some modification) on IR problems. Investigating why this is so, and how to best exploit and extend such methods, provides fertile
Carbonell et al. REF review the emerging research collaborations between the IR and machine learning communities in a special issue of the Machine Learning journal.