Measuring retrieval effectiveness based on user preference of documents

Measuring Retrieval Effectiveness Based on User Preference of Documents

The notion of user preference is adopted for the representation, interpretation and measurement of the relevance or usefulness of documents. User judgments on documents may be formally described by a weak order (i.e., user ranking) and measured using an ordinal scale. Within this framework, a new measure of system performance is suggested based on the distance between user ranking and system ra...

Improving Retrieval Effectiveness by Reranking Documents Based on Controlled Vocabulary

There is a common availability of classification terms in online text collections and digital libraries, such as manually assigned keywords or key-phrases from a controlled vocabulary in scientific collections. Our goal is to explore the use of additional classification information for improving retrieval effectiveness. Earlier research explored the effect of adding classification terms to user...

Supporting Quality-Based Image Retrieval Through User Preference Learning

It is common for modern geospatial libraries to contain multiple datasets that cover the same area but differ only in some specific quality attributes (e.g., resolution and precision). This is affecting the concept of content-based geospatial queries, as simple coverage-based query mechanisms (e.g., declaring a specific area of interest) as well as theme-based query mechanisms (e.g., requesting...

Multiplicative Adaptive Algorithms for User Preference Retrieval

Recommending Web Documents Based on User Preferences

Eric J. Glover1;2 Steve Lawrence1 Michael D. Gordon3 William P. Birmingham2 C. Lee Giles1 fcompuman,lawrence,[email protected] fcompuman,[email protected] [email protected] NEC Research Institute1 Artificial Intelligence Laboratory2 Business Administration3 4 Independence Way University of Michigan University of Michigan Princeton, NJ 0854