There is great interest in supporting imprecise queries over databases today. To support such queries, the system is typically required to disambiguate parts of the user-specified query against the database, using whatever resources are intrinsically available to it (the database schema, value distributions, natural language models etc). Often, systems will also have a user-interaction log available, which can supplement their model based on their own intrinsic resources. This leads to a problem of how best to combine the system's prior ranking with insight derived from the user-interaction log. Statistical inference techniques such as maximum likelihood or Bayesian updates from a subjective prior turn out not to apply in a straightforward way due to possible noise from user search behavior and to encoding biases endemic to the system's models. In this paper, we address such learning problems in interactive data retrieval, with specific focus on type classification for user-specified query terms. We develop a novel Bayesian smoothing algorithm, Bsmooth, which is simple, fast, flexible and accurate. We analytically establish some desirable properties and show, through experiments against an independent benchmark, that the addition of such a learning layer performs much better than standard methods.
. Some ad-hoc practices in the keyword search literature that have been criticized @cite_4 @cite_14 @cite_21 , and that we have strived not to incur in are: Existing scoring functions have become increasingly complex while the added value obscure @cite_21 . We have studied and shown in detail the added value of implicit feedback as a DB-extrinsic scoring to be combined by Bsmooth with any existing DB-intrinsic scoring.
@cite_14 @cite_4 @cite_21
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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. In many application domains of recommender systems, explicit rating information is sparse or non-existent. The preferences of the current user have therefore to be approximated by interpreting his or her behavior, i.e., the implicit user feedback. In the literature, a number of algorithm proposals have been made that rely solely on such implicit feedback, among them Bayesian Personalized Ranking (BPR). In the BPR approach, pairwise comparisons between the items are made in the training phase and an item i is considered to be preferred over item j if the user interacted in some form with i but not with j. In real-world applications, however, implicit feedback is not necessarily limited to such binary decisions as there are, e.g., different types of user actions like item views, cart or purchase actions and there can exist several actions for an item over time. In this paper we show how BPR can be extended to deal with such more fine-granular, graded preference relations. An empirical analysis shows that this extension can help to measurably increase the predictive accuracy of BPR on realistic e-commerce datasets. Implicit feedbacks have recently received much attention in recommendation communities due to their close relationship with real industry problem settings. However, most works only exploit users’ homogeneous implicit feedbacks such as users’ transaction records from “bought” activities, and ignore the other type of implicit feedbacks like examination records from “browsed” activities. The latter are usually more abundant though they are associated with high uncertainty w.r.t. users’ true preferences. In this paper, we study a new recommendation problem called heterogeneous implicit feedbacks (HIF), where the fundamental challenge is the uncertainty of the examination records. As a response, we design a novel preference learning algorithm to learn a confidence for each uncertain examination record with the help of transaction records. Specifically, we generalize Bayesian personalized ranking (BPR), a seminal pairwise learning algorithm for homogeneous implicit feedbacks, and learn the confidence adaptively, which is thus called adaptive Bayesian personalized ranking (ABPR). ABPR has the merits of uncertainty reduction on examination records and accurate pairwise preference learning on implicit feedbacks. Experimental results on two public data sets show that ABPR is able to leverage uncertain examination records effectively, and can achieve better recommendation performance than the state-of-the-art algorithm on various ranking-oriented evaluation metrics. 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.