Retrieval through explanation : an abductive inference approach to relevance feedback

Relevance feedback techniques are designed to automatically improve a system's representation of a query by using documents the user has marked as relevant. However, traditional relevance feedback models suffer from a number of limitations that restrict their potential in supporting information seeking. One of the major limitations of relevance feedback is that it does not incorporate behavioural aspects of information seeking - how and why users assess relevance. We propose that relevance feedback should be viewed as a process of explanation and demonstrate how this limitation of relevance feedback techniques can be overcome by a theory of relevance feedback based on abductive inference

Relevance feedback for best match term weighting algorithms in information retrieval

Personalisation in full text retrieval or full text filtering implies reweighting of the query terms based on some explicit or implicit feedback from the user. Relevance feedback inputs the user's judgements on previously retrieved documents to construct a personalised query or user profile. This paper studies relevance feedback within two probabilistic models of information retrieval: the first based on statistical language models and the second based on the binary independence probabilistic model. The paper shows the resemblance of the approaches to relevance feedback of these models, introduces new approaches to relevance feedback for both models, and evaluates the new relevance feedback algorithms on the TREC collection. The paper shows that there are no significant differences between simple and sophisticated approaches to relevance feedback

Incorporating user search behaviour into relevance feedback

In this paper we present five user experiments on incorporating behavioural information into the relevance feedback process. In particular we concentrate on ranking terms for query expansion and selecting new terms to add to the user's query. Our experiments are an attempt to widen the evidence used for relevance feedback from simply the relevant documents to include information on how users are searching. We show that this information can lead to more successful relevance feedback techniques. We also show that the presentation of relevance feedback to the user is important in the success of relevance feedback

A survey on the use of relevance feedback for information access systems

Users of online search engines often find it difficult to express their need for information in the form of a query. However, if the user can identify examples of the kind of documents they require then they can employ a technique known as relevance feedback. Relevance feedback covers a range of techniques intended to improve a user's query and facilitate retrieval of information relevant to a user's information need. In this paper we survey relevance feedback techniques. We study both automatic techniques, in which the system modifies the user's query, and interactive techniques, in which the user has control over query modification. We also consider specific interfaces to relevance feedback systems and characteristics of searchers that can affect the use and success of relevance feedback systems

Using relevance feedback in expert search

In Enterprise settings, expert search is considered an important task. In this search task, the user has a need for expertise - for instance, they require assistance from someone about a topic of interest. An expert search system assists users with their "expertise need" by suggesting people with relevant expertise to the topic of interest. In this work, we apply an expert search approach that does not explicitly rank candidates in response to a query, but instead implicitly ranks candidates by taking into account a ranking of document with respect to the query topic. Pseudo-relevance feedback, aka query expansion, has been shown to improve retrieval performance in adhoc search tasks. In this work, we investigate to which extent query expansion can be applied in an expert search task to improve the accuracy of the generated ranking of candidates. We define two approaches for query expansion, one based on the initial of ranking of documents for the query topic. The second approach is based on the final ranking of candidates. The aims of this paper are two-fold. Firstly, to determine if query expansion can be successfully applied in the expert search task, and secondly, to ascertain if either of the two forms of query expansion can provide robust, improved retrieval performance. We perform a thorough evaluation contrasting the two query expansion approaches in the context of the TREC 2005 and 2006 Enterprise tracks

Selective relevance feedback using term characteristics

This paper presents a new relevance feedback technique; selectively combining evidence based on the usage of terms within documents. By considering how terms are used within documents, we can better describe the features that might make a document relevant and thus improve retrieval effectiveness. In this paper we present an initial, experimental investigation of this technique, incorporating new and existing measures for describing the information content of a document. The results from these experiments positively support our hypothesis that extending relevance feedback to take into account how terms are used within documents can improve the performance of relevance feedback

Relevance Feedback pada Temu Kembali Teks Berbahasa Indonesia dengan Metode Ide-Dec-Hi dan Ide-Regular

Tujuan penelitian ini adalah mengimplementasikan dan menganalisis kinerja perluasan kueri dengan relevance feedback pada sistem temu kembali informasi untuk dokumen berbahasa Indonesia. Metode relevance feedback yang digunakan adalah Ide-Dec-Hi dan Ide-Regular. Untuk kepentingan pengujian, penelitian ini juga melengkapi corpus yang digunakan dengan 30 kueri disertai gugus jawabannya. Evaluasi kinerja relevance feedback dilakukan menggunakan test and control group. Masing-masing group terdiri atas 500 dokumen yang berupa artikel-artikel pertanian berbahasa Indonesia dari berbagai situs media massa. Sistem dasar yang digunakan adalah sistem temu kembali berbasis vector space model hasil penelitian Ridha (2002). Sistem ini melakukan rule-based stemming sekaligus memakai stoplist untuk bahasa Indonesia. Variasi jumlah dokumen yang diperiksa yakni lima dan sepuluh. Hasil penelitian menunjukkan bahwa relevance feedback secara keseluruhan meningkatkan kinerja sistem temu kembali. Siklus relevance feedback dalam penelitian ini telah menunjukkan hasil memuaskan pada iterasi pertama. Peningkatan kinerja terbesar diperoleh ketika menggunakan formula Ide-Dec-Hi. Hasil ini sesuai dengan hasil penelitian Ruthven & Lalmas (2003). Kinerja sistem tanpa relevance feedback adalah 0.447 sedangkan dengan Ide-Dec-Hi mencapai 0.516, meningkat 15.44%. Sementara menggunakan Ide-Regular peningkatan yang diperoleh adalah 14.54%, menjadi 0.512. Dari perbandingan query-by-query dapat disimpulkan bahwa penggunaan relevance feeback tidak terlalu membantu pada kueri yang kinerja awalnya memang sudah tinggi. Sebaliknya, untuk kueri-kueri yang memberikan hasil buruk pada pencarian awal, relevance feedback sangat cocok untuk digunakan dan menjanjikan peningkatan kinerja yang cukup tinggi

Video information retrieval using objects and ostensive relevance feedback

In this paper, we present a brief overview of current approaches to video information retrieval (IR) and we highlight its limitations and drawbacks in terms of satisfying user needs. We then describe a method of incorporating object-based relevance feedback into video IR which we believe opens up new possibilities for helping users find information in video archives. Following this we describe our own work on shot retrieval from video archives which uses object detection, object-based relevance feedback and a variation of relevance feedback called ostensive RF which is particularly appropriate for this type of retrieval

The use of implicit evidence for relevance feedback in web retrieval

In this paper we report on the application of two contrasting types of relevance feedback for web retrieval. We compare two systems; one using explicit relevance feedback (where searchers explicitly have to mark documents relevant) and one using implicit relevance feedback (where the system endeavours to estimate relevance by mining the searcher's interaction). The feedback is used to update the display according to the user's interaction. Our research focuses on the degree to which implicit evidence of document relevance can be substituted for explicit evidence. We examine the two variations in terms of both user opinion and search effectiveness

Synchronous collaborative information retrieval with relevance feedback

Collaboration has been identified as an important aspect in information seeking. People meet to discuss and share ideas and through this interaction an information need is quite often identified. However the process of resolving this information need, through interacting with a search engine and performing a search task, is still an individual activity. We propose an environment which allows users to collaborate to satisfy a shared information need. We discuss ways to divide the search task amongst collaborators and propose the use of relevance feedback, a common information retrieval process, to enable the transfer of knowledge across collaborators during a search session. We describe the process by which co-searchers can collaborate effectively with little redundancy and how we can combine relevance judgements from multiple searchers into a coherent model for synchronous collaborative information retrieva