personalized recommendations for users of Google News. We generate recommendations using three approaches: collaborative filtering using MinHash clustering, Probabilistic Latent Semantic Indexing (PLSI), and covisitation counts. We combine recommendations from different algorithms using a linear model. Our

personalized recommendations for users of Google News. We generate recommendations using three approaches: collaborative filtering using MinHash clustering, Probabilistic Latent Semantic Indexing (PLSI), and covisitation counts. We combine recommendations from different algorithms using a linear model. Our

. Collaborative filtering approach can efficiently capture user’s behavior in case where the overlap in historical assumptions across users in relatively high and the context universe is almost static. The major issue with the personalized news recommendation system is scalability. This paper addresses the above

Abstract: Online learning initia-tives over the past decade have become increasingly comprehen-sive in their selection of courses and sophisticated in their presen-tation, culminating in the recent announcement of a number of consortium and startup activities that promise to make a university

Prüfungsbehörde vorgelegen. Darmstadt, den 28th February 2014 (Denis Lapiner) This Master’s Thesis proposes and implements a game design for an Android based massively multiplayer online augmented reality game. The proposed game design is based on an analysis of other popular aug-mented reality games

IOS Press Utilization of intelligent agents for supporting citizens in their access to e-government services 1

in partial ful2llment of the requirements for the degree of

of scalability and maintainability. Hence, different approaches are based on Peer-to-Peer (P2P) technology. In particular, fast-paced NVEs stress these systems and most likely cause high overlay maintenance overhead. This Master’s Thesis presents a novel approach to spatial Publish/Subscribe (Pub/Sub) in P2P

on the professional level, but on the personal level as well. Thanks to my colleagues and friends from the Bar-Ilan University in general, and MAVERICK lab in particular, for the great time we had together. Special thanks to Gabriella Melamed, MAVERICK lab manager, for assisting with administra-tive issues

Abstract Supporting database schema evolution repre-sents a long-standing challenge of practical and theoretical importance for modern information systems. In this paper, we describe techniques and systems for automating the critical tasks of migrating the database and rewriting the legacy applications. In addition to labor saving, the ben-efits delivered by these advances are many and include reliable prediction of outcome, minimization of downtime, system-produced documentation, and support for archiving, historical queries, and provenance. The PRISM/PRISM++ system delivers these benefits, by solving the difficult prob-lem of automating the migration of databases and the rewrit-ing of queries and updates. In this paper, we present the PRISM/PRISM++ system and the novel technology that made it possible. In particular, we focus on the difficult and previously unsolved problem of supporting legacy queries and updates under schema and integrity constraints evolution. The PRISM/PRISM++ approach consists in providing the users with a set of SQL-based Schema Modification Opera-tors (SMOs), which describe how the tables in the old schema are modified into those in the new schema. In order to sup-port updates, SMOs are extended with integrity constraints