共查询到20条相似文献，搜索用时 421 毫秒
1.
In this paper, we present ViGOR (Video Grouping, Organisation and Recommendation), an exploratory video retrieval system. Exploratory video retrieval tasks are hampered by the lack of semantics associated to video and the overwhelming amount of video items stored in these types of collections (e.g. YouTube, MSN video, etc.). In order to help facilitate these exploratory video search tasks we present a system that utilises two complementary approaches: the first a new search paradigm that allows the semantic grouping of videos and the second the exploitation of past usage history in order to provide video recommendations. We present two types of recommendation techniques adapted to the grouping search paradigm: the first is a global recommendation, which couples the multi-faceted nature of explorative video retrieval tasks with the current user need of information in order to provide recommendations, and second is a local recommendation, which exploits the organisational features of ViGOR in order to provide more localised recommendations based on a specific aspect of the user task. Two user evaluations were carried out in order to (1) validate the new search paradigm provided by ViGOR, characterised by the grouping functionalities and (2) evaluate the usefulness of the proposed recommendation approaches when integrated into ViGOR. The results of our evaluations show (1) that the grouping, organisational and recommendation functionalities can result in an improvement in the users’ search performance without adversely impacting their perceptions of the system and (2) that both recommendation approaches are relevant to the users at different stages of their search, showing the importance of using multi-faceted recommendations for video retrieval systems and also illustrating the many uses of collaborative recommendations for exploratory video search tasks. 相似文献
2.
3.
In this paper results from three studies examining 1295 relevance judgments by 36 information retrieval (IR) system end-users is reported. Both the region of the relevance judgments, from non-relevant to highly relevant, and the motivations or levels for the relevance judgments are examined. Three major findings are studied. First, the frequency distributions of relevance judgments by IR system end-users tend to take on a bi-modal shape with peaks at the extremes (non-relevant/relevant) with a flatter middle range. Second, the different type of scale (interval or ordinal) used in each study did not alter the shape of the relevance frequency distributions. And third, on an interval scale, the median point of relevance judgment distributions correlates with the point where relevant and partially relevant items begin to be retrieved. The median point of a distribution of relevance judgments may provide a measure of user/IR system interaction to supplement precision/recall measures. The implications of investigation for relevance theory and IR systems evaluation are discussed. 相似文献
4.
The problem of content-based video retrieval continues to pose a challenge to the research community, the performance of video retrieval systems being low due to the semantic gap. In this paper we consider whether taking advantage of context can aid the video retrieval process by making the prediction of relevance easier, i.e. if it is easier for a classification system to predict the relevance of a video shot under a given context, then that context has potential in also improving retrieval, since the underlying features better differentiate relevant from non-relevant video shots. We use an operational definition of context, where datasets can be split into disjoint sub-collections which reflect a particular context. Contexts considered include task difficulty and user expertise, among others. In the classification process, four main types of features are used to represent video-shots: conventional low-level visual features representing physical properties of the video shots, behavioral features which are based on user interaction with the video shots, and two different bag-of-words features obtained from the Automatic Speech Recognition from the audio of the video. 相似文献
5.
《Information processing & management》2001,37(3):435-458
We are interested in how ideas from document clustering can be used to improve the retrieval accuracy of ranked lists in interactive systems. In particular, we are interested in ways to evaluate the effectiveness of such systems to decide how they might best be constructed. In this study, we construct and evaluate systems that present the user with ranked lists and a visualization of inter-document similarities. We first carry out a user study to evaluate the clustering/ranked list combination on instance-oriented retrieval, the task of the TREC-6 Interactive Track. We find that although users generally prefer the combination, they are not able to use it to improve effectiveness. In the second half of this study, we develop and evaluate an approach that more directly combines the ranked list with information from inter-document similarities. Using the TREC collections and relevance judgments, we show that it is possible to realize substantial improvements in effectiveness by doing so, and that although users can use the combined information effectively, the system can provide hints that substantially improve on the user's solo effort. The resulting approach shares much in common with an interactive application of incremental relevance feedback. Throughout this study, we illustrate our work using two prototype systems constructed for these evaluations. The first, AspInQuery, is a classic information retrieval system augmented with a specialized tool for recording information about instances of relevance. The other system, Lighthouse, is a Web-based application that combines a ranked list with a portrayal of inter-document similarity. Lighthouse can work with collections such as TREC, as well as the results of Web search engines. 相似文献
6.
Arthur Taylor Author Vitae 《Information processing & management》2012,48(1):136-153
Relevance judgments occur within an information search process, where time, context and situation can impact the judgments. The determination of relevance is dependent on a number of factors and variables which include the criteria used to determine relevance. The relevance judgment process and the criteria used to make those judgments are manifestations of the cognitive changes which occur during the information search process.Understanding why these relevance criteria choices are made, and how they vary over the information search process can provide important information about the dynamic relevance judgment process. This information can be used to guide the development of more adaptive information retrieval systems which respond to the cognitive changes of users during the information search process.The research data analyzed here was collected in two separate studies which examined a subject’s relevance judgment over an information search process. Statistical analysis was used to examine these results and determine if there were relationships between criteria selections, relevance judgments, and the subject’s progression through the information search process. Findings confirm and extend findings of previous studies, providing strong statistical evidence of an association between the information search process and the choices of relevance criteria by users, and identifying specific changes in the user preferences for specific criteria over the course of the information search process. 相似文献
7.
8.
A new approach to the solicitation and measurement of relevance judgments is presented, which attempts to resolve some of the difficulties inherent in the nature of relevance and human judgment, and which further seeks to examine how users' judgments of document representations change as more information about documents is revealed to them. Subjects (university faculty and doctoral students) viewed three incremental versions of documents, and recorded ratio-level relevance judgments for each version. These judgments were analyzed by a variety of methods, including graphical inspection and examination of the number and degree of changes of judgments as new information is seen. A post questionnaire was also administered to obtain subjects' perceptions of the process and the individual fields of information presented. A consistent pattern of perception and importance of these fields is seen: Abstracts are by far the most important field and have the greatest impact, followed by titles, bibliographic information, and indexing. 相似文献
9.
10.
《Information processing & management》1999,35(3):381-400
The VISION (video indexing for searching over networks) digital video library system has been developed in our laboratory as a testbed for evaluating automatic and comprehensive mechanisms for video archive creation and content-based search, filtering and retrieval of video over local and wide area networks. In order to provide access to video footage within seconds of broadcast, we have developed a new pipelined digital video processing architecture which is capable of digitizing, processing, indexing and compressing video in real time on an inexpensive general purpose computer. These videos were automatically partitioned into short scenes using video, audio and closed-caption information. The resulting scenes are indexed based on their captions and stored in a multimedia database. A client-server-based graphical user interface was developed to enable users to remotely search this archive and view selected video segments over networks of different bandwidths. Additionally, VISION classifies the incoming videos with respect to a taxonomy of categories and will selectively send users videos which match their individual profiles. 相似文献
11.
Some of the most popular measures to evaluate information filtering systems are usually independent of the users because they are based in relevance judgments obtained from experts. On the other hand, the user-centred evaluation allows showing the different impressions that the users have perceived about the system running. This work is focused on discussing the problem of user-centred versus system-centred evaluation of a Web content personalization system where the personalization is based on a user model that stores long term (section, categories and keywords) and short term interests (adapted from user provided feedback). The user-centred evaluation is based on questionnaires filled in by the users before and after using the system and the system-centred evaluation is based on the comparison between ranking of documents, obtained from the application of a multi-tier selection process, and binary relevance judgments collected previously from real users. The user-centred and system-centred evaluations performed with 106 users during 14 working days have provided valuable data concerning the behaviour of the users with respect to issues such as document relevance or the relative importance attributed to different ways of personalization. The results obtained shows general satisfaction on both the personalization processes (selection, adaptation and presentation) and the system as a whole. 相似文献
12.
While test collections provide the cornerstone for Cranfield-based evaluation of information retrieval (IR) systems, it has become practically infeasible to rely on traditional pooling techniques to construct test collections at the scale of today’s massive document collections (e.g., ClueWeb12’s 700M+ Webpages). This has motivated a flurry of studies proposing more cost-effective yet reliable IR evaluation methods. In this paper, we propose a new intelligent topic selection method which reduces the number of search topics (and thereby costly human relevance judgments) needed for reliable IR evaluation. To rigorously assess our method, we integrate previously disparate lines of research on intelligent topic selection and deep vs. shallow judging (i.e., whether it is more cost-effective to collect many relevance judgments for a few topics or a few judgments for many topics). While prior work on intelligent topic selection has never been evaluated against shallow judging baselines, prior work on deep vs. shallow judging has largely argued for shallowed judging, but assuming random topic selection. We argue that for evaluating any topic selection method, ultimately one must ask whether it is actually useful to select topics, or should one simply perform shallow judging over many topics? In seeking a rigorous answer to this over-arching question, we conduct a comprehensive investigation over a set of relevant factors never previously studied together: 1) method of topic selection; 2) the effect of topic familiarity on human judging speed; and 3) how different topic generation processes (requiring varying human effort) impact (i) budget utilization and (ii) the resultant quality of judgments. Experiments on NIST TREC Robust 2003 and Robust 2004 test collections show that not only can we reliably evaluate IR systems with fewer topics, but also that: 1) when topics are intelligently selected, deep judging is often more cost-effective than shallow judging in evaluation reliability; and 2) topic familiarity and topic generation costs greatly impact the evaluation cost vs. reliability trade-off. Our findings challenge conventional wisdom in showing that deep judging is often preferable to shallow judging when topics are selected intelligently. 相似文献
13.
[目的/意义]移动视觉搜索（MVS）服务是移动互联网时代图书馆更好切入用户利用场景和促进图书馆各种资源开发利用的有效措施,而视觉资源与图书馆各种资源的聚合是图书馆MVS服务开展的基础。[方法/过程]从多维度聚合和语义关联两个方面分析了数字资源聚合的理论基础,并从基础数据层、资源描述层、语义聚合层和用户应用层4个方面构建基于语义关联的图书馆MVS资源多维度聚合模型,在此基础上分析了MVS服务的实现流程。[结果/结论]构建移MVS为核心的语义关联视觉资源多维度聚合体系,有利于不同情境下图书馆MVS服务的现实。 相似文献
14.
Examining the generalizability of the User Engagement Scale (UES) in exploratory search 总被引：1，自引：1，他引：0
The user experience is an integral component of interactive information retrieval (IIR). However, there is a twofold problem in its measurement. Firstly, while many IIR studies have relied on a single dimension of user feedback, that of satisfaction, experience is a much more complex concept. IIR in general, and exploratory search more specifically, are dynamic, multifaceted experiences that evoke pragmatic and hedonic needs, expectations, and outcomes that are not adequately captured by user satisfaction. Secondly, questionnaires, which are typically the means in which user’s attitudes and perceptions are measured, are not typically subjected to rigorous reliability and validity testing. To address these issues, we administered the multidimensional User Engagement Scale (UES) in an exploratory search environment to assess users’ perceptions of the Perceived Usability (PUs), Aesthetics (AE), Novelty (NO), Felt Involvement (FI), Focused Attention (FA), and Endurability (EN) aspects of the experience. In a typical laboratory-style study, 381 participants performed three relatively complex search tasks using a novel search interface, and responded to the UES immediately upon completion. We used Principal Axis Factor Analysis and Multiple Regression to examine the factor structure of UES items and the relationships amongst factors. Results showed that three of the six sub-scales (PUs, AE, FA) were stable, while NO, FI and EN merged to form a single factor. We discuss recommendations for revising and validating the UES in light of these findings. 相似文献
15.
16.
Adapting information retrieval to query contexts 总被引：1，自引：0，他引：1
In current IR approaches documents are retrieved only according to the terms specified in the query. The same answers are returned for the same query whatever the user and the search goal are. In reality, many other contextual factors strongly influence document’s relevance and they should be taken into account in IR operations. This paper proposes a method, based on language modeling, to integrate several contextual factors so that document ranking will be adapted to the specific query contexts. We will consider three contextual factors in this paper: the topic domain of the query, the characteristics of the document collection, as well as context words within the query. Each contextual factor is used to generate a new query language model to specify some aspect of the information need. All these query models are then combined together to produce a more complete model for the underlying information need. Our experiments on TREC collections show that each contextual factor can positively influence the IR effectiveness and the combined model results in the highest effectiveness. This study shows that it is both beneficial and feasible to integrate more contextual factors in the current IR practice. 相似文献
17.
David Nicholas Paul Huntington Hamid R. Jamali Anthony Watkinson 《Information processing & management》2006
The article employs deep log analysis (DLA) techniques, a more sophisticated form of transaction log analysis, to demonstrate what usage data can disclose about information seeking behaviour of virtual scholars – academics, and researchers. DLA works with the raw server log data, not the processed, pre-defined and selective data provided by journal publishers. It can generate types of analysis that are not generally available via proprietary web logging software because the software filters out relevant data and makes unhelpful assumptions about the meaning of the data. DLA also enables usage data to be associated with search/navigational and/or user demographic data, hence the name ‘deep’. In this connection the usage of two digital journal libraries, those of EmeraldInsight, and Blackwell Synergy are investigated. The information seeking behaviour of nearly three million users is analyzed in respect to the extent to which they penetrate the site, the number of visits made, as well as the type of items and content they view. The users are broken down by occupation, place of work, type of subscriber (“Big Deal”, non-subscriber, etc.), geographical location, type of university (old and new), referrer link used, and number of items viewed in a session. 相似文献
18.
Jon Rimmer Claire Warwick Ann Blandford Jeremy Gow George Buchanan 《Information processing & management》2008
Traditionally humanities scholars have worked in physical environments and with physical artefacts. Libraries are familiar places, built on cultural traditions over thousands of years, and books are comfortable research companions. Digital tools are a more recent addition to the resources available to a researcher. This paper explores both the physical and the digital qualities of modern humanities research, drawing on existing literature and presenting a study of humanities scholars’ perceptions of the research resources they use. We highlight aspects of the physical and digital that can facilitate or hinder the researcher, focusing on three themes that emerge from the data: the working environment; the experience of finding resources; and the experience of working with documents. Rather than aiming to replace physical texts and libraries by digital surrogates, providers need to recognise the complementary roles they play: digital information environments have the potential to provide improved access and analysis features and the facility to exploit the library from any place, while the physical library and resources provide greater authenticity, trustworthiness and the demand to be in a particular place with important material properties. 相似文献
19.
Web searchers commonly have difficulties crafting queries to fulfill their information needs; even after they are able to craft a query, they often find it challenging to evaluate the results of their Web searches. Sources of these problems include the lack of support for constructing and refining queries, and the static nature of the list-based representations of Web search results. WordBars has been developed to assist users in their Web search and exploration tasks. This system provides a visual representation of the frequencies of the terms found in the first 100 document surrogates returned from an initial query, in the form of a histogram. Exploration of the search results is supported through term selection in the histogram, resulting in a re-sorting of the search results based on the use of the selected terms in the document surrogates. Terms from the histogram can be easily added or removed from the query, generating a new set of search results. Examples illustrate how WordBars can provide valuable support for query refinement and search results exploration, both when vague and specific initial queries are provided. User evaluations with both expert and intermediate Web searchers illustrate the benefits of the interactive exploration features of WordBars in terms of effectiveness as well as subjective measures. Although differences were found in the demographics of these two user groups, both were able to benefit from the features of WordBars. 相似文献
20.
《Information processing & management》2016,52(1):61-72
In recent years, there has been a rapid growth of user-generated data in collaborative tagging (a.k.a. folksonomy-based) systems due to the prevailing of Web 2.0 communities. To effectively assist users to find their desired resources, it is critical to understand user behaviors and preferences. Tag-based profile techniques, which model users and resources by a vector of relevant tags, are widely employed in folksonomy-based systems. This is mainly because that personalized search and recommendations can be facilitated by measuring relevance between user profiles and resource profiles. However, conventional measurements neglect the sentiment aspect of user-generated tags. In fact, tags can be very emotional and subjective, as users usually express their perceptions and feelings about the resources by tags. Therefore, it is necessary to take sentiment relevance into account into measurements. In this paper, we present a novel generic framework SenticRank to incorporate various sentiment information to various sentiment-based information for personalized search by user profiles and resource profiles. In this framework, content-based sentiment ranking and collaborative sentiment ranking methods are proposed to obtain sentiment-based personalized ranking. To the best of our knowledge, this is the first work of integrating sentiment information to address the problem of the personalized tag-based search in collaborative tagging systems. Moreover, we compare the proposed sentiment-based personalized search with baselines in the experiments, the results of which have verified the effectiveness of the proposed framework. In addition, we study the influences by popular sentiment dictionaries, and SenticNet is the most prominent knowledge base to boost the performance of personalized search in folksonomy. 相似文献