An Extended Relevance Model for Session Search

The session search task aims at best serving the user's information need given her previous search behavior during the session. We propose an extended relevance model that captures the user's dynamic information need in the session. Our relevance modelling approach is directly driven by the user's query reformulation (change) decisions and the estimate of how much the user's search behavior affects such decisions. Overall, we demonstrate that, the proposed approach significantly boosts session search performance

Workshop on multimodal crowd sensing (CrowdSens 2012)

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in CIKM '12 Proceedings of the 21st ACM international conference on Information and knowledge management, http://dx.doi.org/10.1145/2396761.2398760.This paper provides an overview of the 1st International Workshop on Multimodal Crowd Sensing (CrowdSens 2012), held at the 21st ACM International Conference on Information and Knowledge Management (CIKM 2012). This workshop aimed to provide an open forum for researchers from various fields such as fields such as Natural Language Processing, Information Extraction, Data Mining, Information Retrieval, User Modeling and Personalization, Stream Processing, and Sensor Networks, for addressing the challenges of effectively mining, analyzing, fusing, and exploiting information sourced from multimodal physical and social sensor data sources.We thank the EU-FP7 project WeGov (grant 248512), and the Spanish project i3media (CENIT-2007-1012) for sponsoring the workshop

A Dual Framework and Algorithms for Targeted Data Delivery

A variety of emerging wide area applications challenge existing techniques for data delivery to users and applications accessing data from multiple autonomous servers. In this paper, we develop a framework for comparing pull based solutions and present dual optimization approaches. Informally, the first approach maximizes user utility of profiles while satisfying constraints on the usage of system resources. The second approach satisfies the utility of user profiles while minimizing the usage of system resources. We present a static optimal solution (SUP) for the latter approach and formally identify sufficient conditions for SUP to be optimal for both. A shortcoming of static solutions to pull-based delivery is that they cannot adapt to the dynamic behavior of Web source updates. Therefore, we present an adaptive algorithm (fbSUP) and show how it can incorporate feedback to improve user utility with only a moderate increase in probing. Using real and synthetic data traces, we analyze the behavior of SUP and fbSUP under various update models