Providing Multilingual Access to Health-Oriented Content

Finding health-related content is not an easy task. People have to know what to search for, which medical terms to use, and where to find accurate information. This task becomes even harder when people such as immigrants wish to find information in their country of residence and do not speak the national language very well. In this paper, we present a new health information system that allows users to search for health information using natural language queries composed of multiple languages. We present the technical details of the system and outline the results of a preliminary user study to demonstrate the usability of the system

Providing multilingual access to health-related content

Finding health-related content is not an easy task. People have to know what to search for, which medical terms to use, and where to find accurate information. This task becomes even harder when people such as immigrants wish to find information in their country of residence and do not speak the national language very well. In this paper, we present a new health information system that allows users to search for health information using natural language queries composed of multiple languages. We present the technical details of the system and outline the results of a preliminary user study to demonstrate the usability of the system

Users and noise: The magic barrier of recommender systems

Abstract. Recommender systems are crucial components of most commercial web sites to keep users satisfied and to increase revenue. Thus, a lot of effort is made to improve recommendation accuracy. But when is the best possible performance of the recommender reached? The magic barrier, refers to some unknown level of prediction accuracy a recommender system can attain. The magic barrier reveals whether there is still room for improving prediction accuracy, or indicates that any further improvement is meaningless. In this work, we present a mathematical characterization of the magic barrier based on the assumption that user ratings are afflicted with inconsistencies -noise. In a case study with a commercial movie recommender, we investigate the inconsistencies of the user ratings and estimate the magic barrier in order to assess the actual quality of the recommender system

An intelligent health assistant for migrants

Preventive health is becoming an increasingly important part of our society. In addition to improving people's personal well being, another positive effect is the reduction of costs for medical treatments that put a high burden on individuals and health care providers. Despite these advantages, preventive health services are still not used at a desired level. This is especially true for people with a migration background, since linguistic or cultural barriers prevent them from accessing preventive health services in their host countries. In this paper, we introduce a system which aims to break down these barriers and to give assistance to migrants

C.: Estimating the magic barrier of recommender systems: a user study

ABSTRACT Recommender systems are commonly evaluated by trying to predict known, withheld, ratings for a set of users. Measures such as the Root-Mean-Square Error are used to estimate the quality of the recommender algorithms. This process does however not acknowledge the inherent rating inconsistencies of users. In this paper we present the first results from a noise measurement user study for estimating the magic barrier of recommender systems conducted on a commercial movie recommendation community. The magic barrier is the expected squared error of the optimal recommendation algorithm, or, the lowest error we can expect from any recommendation algorithm. Our results show that the barrier can be estimated by collecting the opinions of users on already rated items