User's Information Need Research Articles

Bayesian graphical models for adaptive filtering

No abstract available.

Query reformulation using automatically generated query concepts from a document space

We propose a new query reformulation approach, using a set of query concepts that are introduced to precisely denote the user's information need. Since a document collection is considered to be a domain which includes latent primitive concepts, we identify those concepts through a local pattern discovery and a global modeling using data mining techniques. For a new query, we select its most associated primitive concepts and choose the most probable interpretations as query concepts. We discuss the issue of constructing the primitive concepts from either the whole corpus or from the retrieved set of documents. Our experiments are performed on the TREC8 collection. The experimental evaluation shows that our approach is as good as current query reformulation approaches, while being particularly effective for poorly performing queries. Moreover, we find that the approach using the primitive concepts generated from the set of retrieved documents leads to the most effective performance.

Polyphonic music retrieval

This Music Information Retrieval (MIR) study investigates the use of n -grams and textual Information Retrieval (IR) approaches for the retrieval and access of polyphonic music data. IR, synonymous with text IR, implies the task of retrieving documents or texts with information content that is relevant to a user's information need.

A query-based approach in Web search instruction: An assessment of current practice

An instruction program that covers search tools on the Web should incorporate a user's information need as the basis for teaching these tools. This article lays the theoretical groundwork for such a query-based approach, deriving its argument from the literature published in the early days of databases in libraries. A review of recent literature as well as tutorials on the Web reveals that librarians do not routinely consider a user's query in their training in the use of Web-based search tools. Other training elements are more prevalent, in particular the nature of search tools, search strategies and the evaluation of results found in a search. When training recommends a context for searching the Web, the advice tends to be of a general nature. These results suggest that librarians may not routinely be teaching queries as a strategy for selecting and using search tools on the Web. Suggestions for further research are presented to explore these findings.

Research on Information Retrieval System that Supports Keyword Selection based on Generalized Concept and Coverage

It is not easy for a user of an Information Retrieval (IR) system to select an appropriate keyword set to represent his or her specific information need. Therefore, many IR systems can modify keyword sets by estimating the user's particular requirement. Even though such IR systems have better retrieval performance, the complicated estimation process entailed by a large number of keywords makes it difficult for a user to understand how the system behaves. Therefore, we used a thesaurus for query expansion. To select an appropriate keyword set, we proposed two concepts: ``adaptive generalization'', which estimates an appropriate generalization level of the given keywords by using relevant document information, and ``purpose-oriented concept structure modification'', which selects relevant keywords from a predefined synonym set in a thesaurus. Because query expansion based on a thesaurus aims to find new keywords that are complementary to the initial keywords, we proposed to use this method to construct a Boolean query formula to represent the user's information need. We proposed a new IR system called ``appropriate Boolean query reformulation for IR with adaptive generalization'' (ABRIR-AG) to support Boolean query formation. In ABRIR-AG, we reformulated a user-given Boolean query by using a small number of relevant documents. Finally, to evaluate its effectiveness, we evaluated ABRIR-AG by using a large-scale test collection containing WWW documents.

On the query refinement in the ontology-based searching for information

One of the main problems in the (web) information retrieval is the ambiguity of users’ queries, since they tend to post very short queries which do not express their information need clearly. This seems to be valid for the ontology-based information retrieval in which the domain ontology is used as the backbone of the searching process. In this paper, we present a novel approach for determining possible refinements of an ontology-based query. The approach is based on measuring the ambiguity of a query with respect to the original user's information need. We defined several types of the ambiguities concerning the structure of the underlying ontology and the content of the information repository. These ambiguities are interpreted regarding the user's information need, which we infer from the user's behaviour in searching process. Finally, the ranked list of the potentially useful refinements of her query is provided to the user. We present a small evaluation study that shows the advantages of the proposed approach.

Topic modeling for mediated access to very large document collections

AbstractClear and precise queries are a necessity when searching very large document collections, especially when query‐based retrieval is the only means of exploration. We propose system‐mediated information access as a solution for users' well‐documented inability to formulate good queries. Our approach is based on two main assumptions: first, on the ability of document clustering to reveal the topical, semantic structure of a problem domain represented by a specialized “source collection,” and, second, on the capacity of statistical language models to convey content. Taking the role of the human mediator or intermediary searcher, a mediation system interacts with the user and supports her exploration of a relatively small source collection, chosen to be representative for the problem domain. Based on the user's selection of relevant “exemplary” documents and clusters from this source collection, the system builds a language model of her information need. This model is subsequently used to derive “mediated queries,” which are expected to convey precisely and comprehensively the user's information need, and can be submitted by the user to search any large and heterogeneous “target collections.” We present results of experiments that simulated various mediation strategies and compared the effect on mediation effectiveness of a variety of parameters, such as the similarity measure, the weighting scheme, and the clustering method. They provide both upperbounds of performance that can potentially be reached by real end users and a comparison between the effectiveness of these strategies. The experimental evidence suggests that information retrieval mediated through a clustered specialized collection has potential to improve effectiveness significantly.

Structure of domain novice users' queries to a history database

AbstractThis paper presents an information need identification system for interactive information retrieval (IR) for undergraduates researching a history topic, called the INIIReye System. The overall purpose of the INIIReye System is to facilitate domain novice user identification of their information need while they are online interacting with the information store. Here, we give preliminary results from a study that narrows undergraduates' initial topic statements to information need statements. Students may use a faulty accessing point in their queries because before information need identification they base their queries on broad topic terms. We first categorize the type of query terms used by users of an historical database provider, to create a taxonomy of query terms. Next, we use a case study of a history student who visually represents the narrows his essay topic in a series of steps. We conclude that our query taxonomy must include levels of topic specificity because while general topic‐based queries are inappropriate as query terms, more specific topic‐based queries may be closer to the domain novice user's real information need.

Implicit feedback for inferring user preference

article Share on Implicit feedback for inferring user preference: a bibliography Authors: Diane Kelly SCILS, Rutgers University New Brunswick, NJ SCILS, Rutgers University New Brunswick, NJView Profile , Jaime Teevan CSAIL, Massachusetts Institute of Technology, Cambridge, MA CSAIL, Massachusetts Institute of Technology, Cambridge, MAView Profile Authors Info & Claims ACM SIGIR ForumVolume 37Issue 2Fall 2003 pp 18–28https://doi.org/10.1145/959258.959260Online:01 September 2003Publication History 496citation3,950DownloadsMetricsTotal Citations496Total Downloads3,950Last 12 Months105Last 6 weeks16 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

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.

유용성의 관점에서 본 도서관목록의 품질

This study intends to analyze the utility of library catalogs from library users `point of view. Emphasis was on verifying how useful library catalogs are as a tool in order to satisfy the information need of library users. To the end, this study developed the criteria and indicators that can be used to measure the utility of library catalogs from users` viewpoint. On the basis of it, the utility of library catalogs was measured systematically. Sample catalog records for the utility verification were selected from 6 most representative university libraries in Korea. The result of verification was presented and described with the typical examples, and the key factors that caused the low utility of library catalogs were a]so identified and discussed.

Multilateral Digital Library Partnerships for Sharing and Preserving Instructional Content and Context

Two themes are explored relating to Digital Library (DL) development. The first considers partnering as a strategic response to the problem of competitive convergence. The second considers the value added to information by providing what I call here its "context of use." The context of use is of strategic interest to libraries as the critical information need of library users shifts from information access to relief from overwhelming abundance of information. Both themes are explored via a case study of developing electronic course reserves at an academic research library. The case study is significant to academic libraries because it illustrates the degree to which courseware and educational information initiatives are taking place outside the publishing paradigms on which library technical and public services are based. It also highlights how much these initiatives depend on partnering as a strategy. Libraries are challenged both to consider their own DL strategies in light of this context-rich instructional DL infrastructure, and to consider these strategies vis-�-vis an emerging digital university (DU).

Dipe-D: A Tool for Knowledge-Based Query Formulation in Information Retrieval

The paper reports the development of Dipe-D, a knowledge-based procedure for the formulation of Boolean queries in information retrieval. Dipe-D creates a query in two steps: (1) the user's information need is developed interactively, while identifying the concepts of the information need, and subsequently (2) the collection of concepts identified is automatically transformed into a Boolean query. In the first step, the subject area—as represented in a knowledge base—is explored by the user. He does this by means of specifying the (concepts that meet his) information need in an artificial language and looking through the solution as provided by the computer. The specification language allows one to specify concepts by their features, both in precise terms as well as vaguely. By repeating the process of specifying the information need and exploring the resulting concepts, the user may precisely single out the concepts that describe his information need. In the second step, the program provides the designations (and variants) for the concepts identified, and connects them by appropriate operators. Dipe-D is meant to improve on existing procedures that identify the concepts less systematically, create a query manually, and then sometimes expand that query. Experiments are reported on each of the two stepss they indicate that the first step identifies only but not all the relevant concepts, and the second step performs (at least) as good as human beings do.

An information filtering model on the Web and its application in JobAgent

Machine-learning techniques play the important roles for information filtering. The main objective of machine-learning is to obtain users' profiles. To decrease the burden of on-line learning, it is important to seek suitable structures to represent user information needs. This paper proposes a model for information filtering on the Web. The user information need is described into two levels in this model: profiles on category level, and Boolean queries on document level. To efficiently estimate the relevance between the user information need and documents, the user information need is treated as a rough set on the space of documents. The rough set decision theory is used to classify the new documents according to the user information need. In return for this, the new documents are divided into three parts: positive region, boundary region, and negative region. An experimental system JobAgent is also presented to verify this model, and it shows that the rough set based model can provide an efficient approach to solve the information overload problem.

User Modeling for Adaptive News Access

We present a framework for adaptive news access, based on machine learning techniques specifically designed for this task. First, we focus on the system's general functionality and system architecture. We then describe the interface and design of two deployed news agents that are part of the described architecture. While the first agent provides personalized news through a web-based interface, the second system is geared towards wireless information devices such as PDAs (personal digital assistants) and cell phones. Based on implicit and explicit user feedback, our agents use a machine learning algorithm to induce individual user models. Motivated by general shortcomings of other user modeling systems for Information Retrieval applications, as well as the specific requirements of news classification, we propose the induction of hybrid user models that consist of separate models for short-term and long-term interests. Furthermore, we illustrate how the described algorithm can be used to address an important issue that has thus far received little attention in the Information Retrieval community: a user's information need changes as a direct result of interaction with information. We empirically evaluate the system's performance based on data collected from regular system users. The goal of the evaluation is not only to understand the performance contributions of the algorithm's individual components, but also to assess the overall utility of the proposed user modeling techniques from a user perspective. Our results provide empirical evidence for the utility of the hybrid user model, and suggest that effective personalization can be achieved without requiring any extra effort from the user.

Interaction with an enabling information retrieval system: Modeling the user's decoding and encoding operations

With new interactive technology, we can increase user satisfaction by designing information retrieval systems that inform the user while the user is on-line interacting with the system. The purpose of this article is to model the information processing operations of a generic user who has just received an informative message from the system and is stimulated by the message into grasping at a higher understanding of his or her information task or problem. The model consists of three levels, each of which forms a separate subsystem. In the Perception subsystem, the user perceives the system message in a visual sense; in the Comprehension subsystem, the user must comprehend the system message; and in the Application subsystem, the user must (a) interpret the system message in terms of the user's task at hand, and (b) create and send a new message back to the system to complete the interaction. Because of the information process stimulated by the interaction, the user's new message forms a query to the system that more accurately represents the user's information need than would have been the case if the interaction had not taken place. This article proposes a device to enable clarification of the user's task, and thus his/her information need at the Application subsystem level of the model.

OUT on Loan: A Survey of the Use and Information Needs of Users of the Lesbian, Gay and Bisexual Collection of Brighton and Hove Libraries

OUT on Loan: A Survey of the Use and Information Needs of Users of the Lesbian, Gay and Bisexual Collection of Brighton and Hove Libraries

The Text REtrieval Conferences (TRECs): Providing a Test‐Bed for Information Retrieval Systems

Les congres de la TREC (Text REtrieval Conferences) ont pour sujet d'etude les methodes de recherche d'information ainsi que l'evaluation de ces methodes. La TREC fournit des collection-test

Relevance feedback: a power tool for interactive content-based image retrieval

Content-based image retrieval (CBIR) has become one of the most active research areas in the past few years. Many visual feature representations have been explored and many systems built. While these research efforts establish the basis of CBIR, the usefulness of the proposed approaches is limited. Specifically, these efforts have relatively ignored two distinct characteristics of CBIR systems: (1) the gap between high-level concepts and low-level features, and (2) the subjectivity of human perception of visual content. This paper proposes a relevance feedback based interactive retrieval approach, which effectively takes into account the above two characteristics in CBIR. During the retrieval process, the user's high-level query and perception subjectivity are captured by dynamically updated weights based on the user's feedback. The experimental results over more than 70000 images show that the proposed approach greatly reduces the user's effort of composing a query, and captures the user's information need more precisely.

A Methodology for the Identification of Information Needs of Users

L'identification des besoins d'informations est essentielle a la conception des systemes d'informations en general et a la prestation des services d'informations efficaces en particulier. Mais la tâche est difficile dans la mesure ou elle se confond presque avec un travail d'investigation ou de detection. Pour la mener a bien, force est d'adopter plusiers methodes afin de rassembler un maximum d'informations sur les multiples facteurs qui determinent le besoin d'informations. Aucun procede, aucun outil ne saurait y suffire seul. Il est indispensable d'associer des techniques differentes, rigoureusement selectionnees au prealable en fonction de la categorie d'usagers etudiee. Les personnes chargees d'identifier les besoins d'informations doivent donc soigneusement se documenter et se preparer. L'article expose les etapes successives d'un protocole pouvant servir a etudier les besoins d'informations d'une majorite d'usagers. Une fois les donnees collectees et consignees, il convient de proceder a une analyse approfondie afin de degager les besoins reels de l'ensemble des elements reunis. Le souhait des auteurs est que la methodologie ici presentee soit facile a apprehender et a mettre en pratique. Ils soulignent qu'elle gagne progressivement en clarte a chaque etape, d'ou une meilleure comprehension du scenario d'ensemble qui permet d'ajuster au plus pres le protocole en fonction des situations particulieres rencontrees. Il apparait en outre que l'utilite de cette methodologie ne se limite pas a la seule identification des besoins d'informations puisqu'elle determine dans une large mesure les moyens a mettre en oeuvre pour y repondre. En d'autres termes, les personnes chargees de l'identification decouvrent parallelement nombre d'idees, d'outils, de methodes et de techniques qui leur permettent de donner satisfaction aux usagers en repondant a leurs besoins.