This paper presents a new user feedback mechanism based on Wikipedia concepts for interactive retrieval. In this mechanism, the system presents to the user a group of Wikipedia concepts, and the user can choose those relevant to refine his her query. To realize this mechanism, we propose methods to address two problems: 1) how to select a small number of possibly relevant Wikipedia concepts to show the user, and 2) how to re-rank retrieved documents given the user-identified Wikipedia concepts. Our methods are evaluated on three TREC data sets. The experiment results show that our methods can dramatically improve retrieval performances.
Different types of user feedback have been shown useful for ad-hoc retrieval, including document-based relevance feedback @cite_14 @cite_7 @cite_0 , term-based feedback @cite_13 , metadata-based faceted feedback @cite_15 @cite_10 @cite_1 @cite_2 . In this paper, we study a new type of user feedback based on Wikipedia concepts and show that this type of feedback can be very useful for retrieval.
@cite_14 @cite_7 @cite_10 @cite_1 @cite_0 @cite_2 @cite_15 @cite_13
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Motivated by the commonly used faceted search interface in e-commerce, this paper investigates interactive relevance feedback mechanism based on faceted document metadata. In this mechanism, the system recommends a group of document facet-value pairs, and lets users select relevant ones to restrict the returned documents. We propose four facet-value pair recommendation approaches and two retrieval models that incorporate user feedback on document facets. Evaluated based on user feedback collected through Amazon Mechanical Turk, our experimental results show that the Boolean filtering approach, which is widely used in faceted search in e-commerce, doesn't work well for text document retrieval, due to the incompleteness (low recall) of metadata assignment in semi-structured text documents. Instead, a soft model performs more effectively. The faceted feedback mechanism can also be combined with document-based relevance feedback and pseudo relevance feedback to further improve the retrieval performance. In this paper we propose an implicit relevance feedback method with the aim to improve the performance of known Content Based Image Retrieval (CBIR) systems by re-ranking the retrieved images according to users' eye gaze data. This represents a new mechanism for implicit relevance feedback, in fact usually the sources taken into account for image retrieval are based on the natural behavior of the user in his her environment estimated by analyzing mouse and keyboard interactions. In detail, after the retrieval of the images by querying CBIRs with a keyword, our system computes the most salient regions (where users look with a greater interest) of the retrieved images by gathering data from an unobtrusive eye tracker, such as Tobii T60. According to the features, in terms of color, texture, of these relevant regions our system is able to re-rank the images, initially, retrieved by the CBIR. Performance evaluation, carried out on a set of 30 users by using Google Images and "pyramid" like keyword, shows that about the 87 of the users is more satisfied of the output images when the re-raking is applied. Pseudo-relevance feedback has proven to be an effective strategy for improving retrieval accuracy in all retrieval models. However the performance of existing pseudo feedback methods is often affected significantly by some parameters, such as the number of feedback documents to use and the relative weight of original query terms; these parameters generally have to be set by trial-and-error without any guidance. In this paper, we present a more robust method for pseudo feedback based on statistical language models. Our main idea is to integrate the original query with feedback documents in a single probabilistic mixture model and regularize the estimation of the language model parameters in the model so that the information in the feedback documents can be gradually added to the original query. Unlike most existing feedback methods, our new method has no parameter to tune. Experiment results on two representative data sets show that the new method is significantly more robust than a state-of-the-art baseline language modeling approach for feedback with comparable or better retrieval accuracy. User feedback is considered to be a critical element in the information seeking process. An important aspect of the feedback cycle is relevance assessment that has progressively become a popular practice in web searching activities and interactive information retrieval (IR). The value of relevance assessment lies in the disambiguation of the user's information need, which is achieved by applying various feedback techniques. Such techniques vary from explicit to implicit and help determine the relevance of the retrieved documents. The former type of feedback is usually obtained through the explicit and intended indication of documents as relevant (positive feedback) or irrelevant (negative feedback). Explicit feedback is a robust method for improving a system's overall retrieval performance and producing better query reformulations [1], at the expense of users' cognitive resources. On the other hand, implicit feedback techniques tend to collect information on search behavior in a more intelligent and unobtrusive manner. By doing so, they disengage the users from the cognitive burden of document rating and relevance judgments. Information-seeking activities such as reading time, saving, printing, selecting and referencing have been all treated as indicators of relevance, despite the lack of sufficient evidence to support their effectiveness [2]. Besides their apparent differences, both categories of feedback techniques determine document relevance with respect to the cognitive and situational levels of the interactive dialogue that occurs between the user and the retrieval system [5]. However, this approach does not account for the dynamic interplay and adaptation that takes place between the different dialogue levels, but most importantly it does not consider the affective dimension of interaction. Users interact with intentions, motivations and feelings apart from real-life problems and information objects, which are all critical aspects of cognition and decision-making [3][4]. By evaluating users' affective response towards an information object (e.g. a document), prior and post to their exposure to it, a more accurate understanding of the object's properties and degree of relevance to the current information need may be facilitated. Furthermore, systems that can detect and respond accordingly to user emotions could potentially improve the naturalness of human-computer interaction and progressively optimize their retrieval strategy. The current study investigates the role of emotions in the information seeking process, as the latter are communicated through multi-modal interaction, and reconsiders relevance feedback with respect to what occurs on the affective level of interaction as well.