ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems

Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine towards accomplishing the task of interest. The learning machine leverages big data to find examples that maximize the training value of its interaction with the teacher. When the teacher is restricted to labeling examples selected by the machine, this problem is an instance of active learning. When the teacher can provide additional information to the machine (e.g., suggestions on what examples or predictive features should be used) as the learning task progresses, then the problem becomes one of interactive learning. To accommodate the two-way communication channel needed for efficient interactive learning, the teacher and the machine need an environment that supports an interaction language. The machine can access, process, and summarize more examples than the teacher can see in a lifetime. Based on the machine's output, the teacher can revise the definition of the task or make it more precise. Both the teacher and the machine continuously learn and benefit from the interaction. We have built a platform to (1) produce valuable and deployable models and (2) support research on both the machine learning and user interface challenges of the interactive learning problem. The platform relies on a dedicated, low-latency, distributed, in-memory architecture that allows us to construct web-scale learning machines with quick interaction speed. The purpose of this paper is to describe this architecture and demonstrate how it supports our research efforts. Preliminary results are presented as illustrations of the architecture but are not the primary focus of the paper

An approach to graph-based analysis of textual documents

In this paper a new graph-based model is proposed for the representation of textual documents. Graph-structures are obtained from textual documents by making use of the well-known Part-Of-Speech (POS) tagging technique. More specifically, a simple rule-based (re) classifier is used to map each tag onto graph vertices and edges. As a result, a decomposition of textual documents is obtained where tokens are automatically parsed and attached to either a vertex or an edge. It is shown how textual documents can be aggregated through their graph-structures and finally, it is shown how vertex-ranking methods can be used to find relevant tokens.(1)

Right here, right now: situated interventions to change consumer habits

Consumer behavior-change interventions have traditionally encouraged consumers to form conscious intentions, but in the past decade it has been shown that while these interventions have a medium-to-large effect in changing intentions, they have a much smaller effect in changing behavior. Consumers often do not act in accordance with their conscious intentions because situational cues in the immediate environment automatically elicit learned, habitual behaviors. It has therefore been suggested that researchers refocus their efforts on developing interventions that target unconscious, unintentional influences on behavior, such as cue-behavior (“habit”) associations. To develop effective consumer behavior-change interventions, however, we argue that it is first important to understand how consumer experiences are represented in memory, in order to successfully target the situational cues that most strongly predict engagement in habitual behavior. In this article, we present a situated cognition perspective of habits and discuss how the situated cognition perspective extends our understanding of how consumer experiences are represented in memory, and the processes through which these situational representations can be retrieved in order to elicit habitual consumer behaviors. Based on the principles of situated cognition, we then discuss five ways that interventions could change consumer habits by targeting situational cues in the consumer environment and suggest how existing interventions utilizing these behavior-change strategies could be improved by integrating the principles of the situated cognition approach