Loren G Terveen
Biographic Data
| ID | 10733442 |
|---|---|
| NAME | Loren G Terveen |
| GIVEN NAMES | Loren G |
| FAMILY NAME | Terveen |
| SIGNATURE | TERVEEN L G |
| AFFILIATIONS | AT&T Labs - Research, 180 Park Avenue, Florham Park, NJ |
| VERIFIED | No |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 1995 |
| LATEST PUBLICATION YEAR | 2004 |
| H-INDEX | 0 |
Evaluating collaborative filtering recommender systems
Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing t…
Conference preview
No abstract available
Living Design Memory
We identify an important type of software design knowledge that we call community-specific folklore and discuss problems with current approaches to managing it. We developed a general framework for a living design memory, built a design memory tool, and deployed the tool in a large software development organization. The tool effectively disseminates knowledge relevant to local software design practice. It is embedded in the organizational process…
No prominent works on this page.
Living Design Memory
We identify an important type of software design knowledge that we call community-specific folklore and discuss problems with current approaches to managing it. We developed a general framework for a living design memory, built a design memory tool, and deployed the tool in a large software development organization. The tool effectively disseminates knowledge relevant to local software design practice. It is embedded in the organizational process…
Conference preview
No abstract available
Evaluating collaborative filtering recommender systems
Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing t…
Computer Science (3 works) · Advanced Bandit Algorithms Research (1 work) · Artificial Intelligence (1 work) · Business Process Modeling and Analysis (1 work) · Class (philosophy) (1 work) · Collaborative filtering (1 work) · Data mining (1 work) · Diverse Scientific and Economic Studies (1 work) · Domain (mathematical analysis) (1 work) · Educational Robotics and Engineering (1 work)