"Abstract: Recommendation systems are applied to personalize and customize the Web environment. We have developed a recommendation system, termed Yoda, that is designed to support large-scale Web-based applications requiring highly accurate recommendations in real-time. With Yoda, we introduce a hybrid approach that combines collaborative filtering (CF) and content-based querying to achieve higher accuracy. Yoda is structured as a tunable model that is trained off-line and employed for real-time recommendation on-line. The on-line process benefits from an optimized aggregation function with low complexity that allows real-time weighted aggregation of the soft classification of active users to predefned recommendation sets. Leveraging on localized distribution of the recommendable items, the same aggregation function is further optimized for the off-line process to reduce the time complexity of constructing the pre-defined recommendation sets of the model. To make the off-line process scalable furthermore, we also propose a filtering mechanism, FLSH, that extends the Locality Sensitive Hashing technique by incorporating a novel distance measure that satisfies specific requirements of our application. Our end-to-end experiments show while Yoda's complexity is low and remains constant as the number of users and/or items grow, its accuracy surpasses that of the basic nearest-neighbor method by a wide margin (in most cases more than 100%)."

Yoda: An Accurate and Scalable Web-based Recommendation System [PDF]

Held in Italy in September. Papers of interest:

Interactive Integration of Information Agents on the Web
CoWing: A Collaborative Bookmark Management System
Context Aware Agents for Personal Information Services
Decision Trees for Multiple Abstraction Levels of Data
Supporting Information Integration with Autonomous Agents
Domain-Independent Ontologies for Cooperative Information Agents
Arms Race within Information Ecosystems
Information Agents: The Social Nature of Information and the Role of Trust
Ontology Negotiation as a Basis for Opportunistic Cooperation between Intelligent Information Agents

LNAI 2182: Cooperative Information Agents V

"This specification provides a model and grammar for representing the structure of information resources used to define topics, and the associations (relationships) between topics. Names, resources, and relationships are said to be characteristics of abstract subjects, which are called topics. Topics have their characteristics within scopes: i.e. the limited contexts within which the names and resources are regarded as their name, resource, and relationship characteristics. One or more interrelated documents employing this grammar is called a “topic map.”"

XML Topic Maps (XTM) 1.0

The Dublin Core provides an excellent baseline data structure for an online recommender system.

Dublin Core Metadata Element Set: ANSI/NISO Z39.85-2001