Improving Information Retrieval Effectiveness in Peer-to-Peer Networks through Query Piggybacking

Περιέχει το πλήρες κείμενοThis work describes an algorithm which aims at increasing the quantity of relevant documents retrieved from a Peer-To-Peer (P2P) network. The algorithm is based on a statistical model used for ranking documents, peers and ultra-peers, and on a “piggybacking” technique performed when the query is routed across the network. The algorithm “amplifies” the statistical information about the neighborhood stored in each ultra-peer. The preliminary experiments provided encouraging results as the quantity of relevant documents retrieved through the network almost doubles once query piggybacking is exploited

Evaluation of a Recursive Weighting Scheme for Federated Web Search

The informative resources available on the Web are not always directly accessible and cannot therefore be crawled since access is permitted only through the adoption of appropriate services, e.g. specialized search engines. On the other hand, specialized search engines can help address the problem of heterogeneity of the informative resources due to the type of content, the structure or the media. Federated Web Search systems address the problem of searching multiple, heterogeneous, and possibly uncooperative collections. One issue of Federated Web Search is resource selection, i.e. the selection of the search engines which most likely provide documents relevant to the query. This paper reports on the experimental evaluation in Federated Web Search setting of a recursive weighting scheme for ranking informative resources in architectures that involve an arbitrary number of resource levels

i-TEL-u: A Query Suggestion Tool for Integrating Heterogeneous Contexts in a Digital Library

none5This paper presents the design, implementation and evaluation of a query suggestion tool (named i-TEL-u) that allows for the management and the exploitation of different contexts in an integrated way within the same search interface for accessing the contents of The European Library portal\footnote{http://www.theeuropeanlibrary.org/}. i-TEL-u allows users to seamlessly move from one context to another according to their information needs and to the way these needs evolve during the search session. The aim of this tool is to improve the search functionalities of the portal, attract many users and give them easy and effective access\footnote{The work was partially supported by TELplus Targeted Project - eContent\emph{plus} Program of the European Commission (Contract ECP-2006-DILI-510003).}noneAGOSTI M.; D. CISCO; G. M. DI NUNZIO; I. MASIERO; M. MELUCCIAgosti, Maristella; D., Cisco; DI NUNZIO, GIORGIO MARIA; Masiero, Ivano; Melucci, Massim

To Re-rank or to Re-query: Can Visual Analytics Solve This Dilemma?

Evaluation has a crucial role in Information Retrieval (IR) since it allows possible point of failures of an IR approach to be identi ed and addressed thus improving the predictive capability of such approach. Developing tools to support users when analyzing results and investigating strategies to improve IR system performance can help make the analysis easier and more eective. In this paper we discuss a Visual Analytics-based approach to support the user when deciding whether or not to perform re-ranking to improve the system eectiveness measured after a retrieval run. The proposed approach is based on eectiveness measures that exploit graded relevance judgements and provide both a principled and intuitive way to support the user. A prototype is described and exploited to discuss some case studies based on TREC data

Interactive Analysis and Exploration of Experimental Evaluation Results

This paper proposes a methodology based on discounted cumulated gain measures and visual analytics techniques in order to improve the analysis and understanding of IR experimental evaluation results. The proposed methodology is geared to favour a natural and eective interaction of the researchers and developers with the experimental data and it is demonstrated by developing an innovative application based on Apple iPad

To re-rank or to re-query: Can visual analytics solve this dilemma?

Evaluation has a crucial role in (IR) since it allows for identifying possible points of failure of an IR approach, thus addressing them to improve its effectiveness. Developing tools to support researchers and analysts when analyzing results and investigating strategies to improve IR system performance can help make the analysis easier and more effective. In this paper we discuss a VA-based approach to support the analyst when deciding whether or not to investigate re-ranking to improve the system effectiveness measured after a retrieval run. Our approach is based on effectiveness measures that exploit graded relevance judgements and it provides both a principled and intuitive way to support analysis. A prototype is described and exploited to discuss some case studies based on TREC data. © 2011 Springer-Verlag