Recommender systems have been part of the Internet for almost two decades. Dozens of vendors have built recommendation technologies and taken them to market in two waves, roughly aligning with the web 1.0 and 2.0 revolutions. Today recommender systems are found in a multitude of online services. They have been developed using a variety of techniques and user interfaces. They have been nurtured with millions of users’ explicit and implicit preferences (most often with their permission). Frequently they provide relevant recommendations that increase the revenue or user engagement of the online services that operate them. However, when we evaluate the current generation of recommender systems from the point of view of the “recommendee,” we find that most recommender systems serve the goals of the business instead of their users’ interests. Thus we believe that the big promise of recommender systems has yet to be fulfilled. We foresee a third wave of recommender systems that act directly on behalf of their users across a range of domains instead of acting as a sales assistant. We also predict that such new recommender systems will better deal with information overload, take advantage of contextual clues from mobile devices, and utilize the vast information and computation stores available through cloud-computing services to maximize users’ long-term goals

This paper describes how pedigree is used to support and enhance situation and threat assessment. It is based on the findings of the technology group of the Data Fusion Levels Two and Three Workshop sponsored by the Office of Naval Research held in Arlington, VA from 15-18 Nov. 2005. It identifies areas that need improvement in situation assessment and threat assessment, such as interoperability, automation, pedigree management, system usability, reliability, and uncertainty. The concept of pedigree must include "standard" metadata, lineage, plus a computational model of the quality of the information. The system must automatically propagate changes and update to derived products when source information or sourcepedigree information changes. Several other processes must be automated: generate pedigree, identify and auto fill gaps, fuse pedigree, update pedigree, display of information quality and confidence. The paper concludes with suggestions for future research and development.