Bhaskar Mehta

Ontologically-Enriched unified user modeling for cross-system personalization

Personalization today has wide spread use on many Web sites. Systems and applications store preferences and information about users in order to provide personalized access. However, these systems store user profiles in proprietary formats. Although some of these systems store similar information about the user, exchange or reuse of information is not possible and information is duplicated. Additionally, since user profiles tend to be deeply buried inside such systems, users have little control over them. This paper proposes the use of a common ontology-based user context model as a basis for the exchange of user profiles between multiple systems and, thus, as a foundation for cross-system personalization.

Learning from What Others Know: Privacy Preserving Cross System Personalization

Recommender systems have been steadily gaining popularity and have been deployed by several service providers. Large scalable deployment has however highlighted one of the design problems of recommender systems: lack of interoperability. Users today often use multiple electronic systems offering recommendations, which cannot learn from one another. The result is that the end user has to often provide similar information and in some cases disjoint information. Intuitively, it seems that much can be improved with this situation: information learnt by one system could potentially be reused by another, to offer an overall improved personalization experience. In this paper, we provide an effective solution to this problem using Latent Semantic Models by learning a user model across multiple systems. A privacy preserving distributed framework is added around the traditional Probabilistic Latent Semantic Analysis framework, and practical aspects such as addition of new systems and items are also dealt with in this work.

An Architecture for Recommendation Based Service Mediation

Resource brokering is a crucial activity in Grid infrastructures and other environments with dynamic resource selection. The mediation between resource requirements and available resources relies on adequate resource description, which becomes a special challenge when the resources are (Grid) services.

The role of context for information mediation in digital libraries

Mediating between available information objects and individual information needs is a central issue within the functionality of a digital library. In the simplest case this is an information request answered by a search engine based on an analysis of information objects within the digital librarys information collection. However, neither the information access activity nor the information objects within the collection are isolated entities. They are both equipped with a multifaceted context. The invited talk, which is summarized by this paper, analyzes this context and discusses complementing approaches to make such context explicit and to use it for refining the mediation process within digital libraries.

The role of context for information mediation

Mediating between available information objects and individual information needs is a central issue within the information society. In the simplest case this is an information request answered by a search engine based on the analysis of the content of individual information objects within an information collection that may be a digital library, but also the World Wide Web. However, neither the information search activity nor the information objects within the collection are isolated entities. They are both equipped with a multifaceted context. This paper discusses complementing ways to make such context explicit and to use it to improve the information mediation process.