In the area of information retrieval the concept of relevance feedback is used to provide high relevant documents to the user. The process of gaining relevance data is usually based on explicit Relevance Feedback. But it turned out, that users are usually not willing to provide such data. This paper describes a Relevance Feedback approach that supports the users with query expansion terms by us...

Various Relevance Feedback techniques exist in Information Retrieval such as Simulated Relevance Feedback and Pseudo Relevance Feedback. In a Simulated Relevance Feedback technique a new query is reformulated based on the documents selected by the user from the top-ranked documents whereas in a Pseudo Relevance Feedback, the query is reformulated based on the assumption that N top-ranked docume...

This paper describes our participation at ImageCLEF 2009. We participated in the photographic retrieval task (ImageCLEFPhoto). Our method is based on intermedia pseudo-relevance feedback. We have enhanced the pseudo-relevance feedback mechanism by using semantic selectional restrictions. We use Terrier for text retrieval and our own simple block-based visual retrieval engine. The results obtain...

Experiments on the effectiveness of relevance feedback with real users are time-consuming and expensive. This makes simulation for rapid testing desirable. We define a user model, which helps to quantify some interaction decisions involved in simulated relevance feedback. First, the relevance criterion defines the relevance threshold of the user to accept documents as relevant to his/her needs....

The relevance feedback track in TREC 2009 focuses on two sub tasks: actively selecting good documents for users to provide relevance feedback and retrieving documents based on user relevance feedback. For the first task, we tried a clustering based method and the Transductive Experimental Design (TED) method proposed by Yu et al. [5]. For clustering based method, we use the K-means algorithm to...

Successfully retrieving a web document is a twofold problem: having an adequate query that can usefully and properly help filtering relevant documents from huge collections, and presenting the user those that may indeed fulfill his/her needs. In this paper, we focus on the first issue – the problem of having a misleading user query. The aim of the work is to refine a query by using extracts ins...

We extended language modeling approaches in information retrieval (IR) to combine collaborative filtering (CF) and content-based filtering (CBF). Our approach is based on the analogy between IR and CF, especially between CF and relevance feedback (RF). Both CF and RF exploit users’ preference/relevance judgments to recommend items. We first introduce a multinomial model that combines CF and CBF...

1. Relevance feedback Relevance feedback (RF) is an interactive technique that is intended to automatically improve an information retrieval (IR) system's representation of a search based on documents that a user has assessed as being relevant. One strength of RF is that it requires minimal input from a user: a user only has to indicate relevant material they do not have to describe what makes ...

Highly heterogeneous XML data collections that do not have a global schema, as arising, for example, in federations of digital libraries or scientific data repositories, cannot be effectively queried with XQuery or XPath alone, but rather require a ranked retrieval approach. As known from ample work in the IR field, relevance feedback provided by the user that drives automatic query refinement ...

We propose a structure cognizant framework for pseudo relevance feedback (PRF). This has an application, for example, in selecting expansion terms for general search from subsets such as Wikipedia, wherein documents typically have a minimally fixed set of fields, viz., Title, Body, Infobox and Categories. In existing approaches to PRF based expansion, weights of expansion terms do not depend on...