There is a large literature explaining why AdaBoost is a successful classifier. The literature on AdaBoost focuses on classifier margins and boosting's interpretation as the optimization of an exponential likelihood function. These existing explanations, however, have been pointed out to be incomplete. A random forest is another popular ensemble method for which there is substantially less explanation in the literature. We introduce a novel perspective on AdaBoost and random forests that proposes that the two algorithms work for similar reasons. While both classifiers achieve similar predictive accuracy, random forests cannot be conceived as a direct optimization procedure. Rather, random forests is a self- averaging, interpolating algorithm which creates what we denote as a spiked-smooth classifier, and we view AdaBoost in the same light. We conjecture that both AdaBoost and random forests succeed because of this mechanism. We provide a number of examples to support this explanation. In the process, we question the conventional wisdom that suggests that boosting algorithms for classification require regularization or early stopping and should be limited to low complexity classes of learners, such as decision stumps. We conclude that boosting should be used like random forests: with large decision trees, without regularization or early stopping.

The query-document relevance judgments used in web search engine evaluation are traditionally provided by human assessors who have no particular association with the specific queries selected for the evaluation. Most commonly, queries are randomly sampled from search logs and in turn randomly assigned to the human assessors. In this paper, we consider a very different approach in which we instead ask the human assessors to provide their own queries from their recent search experiences. Using these queries as our sample, we compare the relevance judgments from the "owners" of the queries to the relevance judgments of the non-owners.

Web search performance can be improved by either improving the search engine itself or by educating the user to search more efficiently. There is a large amount of literature describing techniques for measuring the former; whereas, improvements resulting from the latter are more difficult to quantify. In this paper we demonstrate an experimental methodology that proves to successfully quantify improvements from user education. The user education in our study is realized in the form of tactical search feature tips that expand user awareness of task-relevant tools and features of the search application. Initially, these tips are presented in an idealized situation: each tip is shown at the same time as the study participants are given a task that is constructed to benefit from the specific tip. However, we also present a follow-up study roughly one week later in which the search tips are no longer presented but the study participants who previously were shown search tips still demonstrate improved search efficiency compared to the control group. This research has implications for search user interface designers and the study of information retrieval systems.

We consider experiments to measure the quality of a web search algorithm based on how much total time users take to complete assigned search tasks using that algorithm. We first analyze our data to verify that there is in fact a negative relationship between a user's total search time and a user's satisfaction for the types of tasks under consideration. Secondly, we fit a model with the user's total search time as the response to compare two different search algorithms. Finally, we propose an alternative experimental design which we demonstrate to be a substantial improvement over our current design in terms of variance reduction and efficiency.