Multiple search sessions model of end-user behavior: An exploratory study

The dominant existing routing strategies employed in peer-to-peer(P2P) based information retrieval(IR) systems are similarity-based approaches. In these approaches, agents depend on the content similarity between incoming queries and their direct neighboring agents to direct the distributed search sessions. However, such a heuristic is myopic in that the neighboring agents may not be connected to more relevant agents. In this paper, an online reinforcement-learning based approach is developed to take advantage of the dynamic run-time characteristics of P2P IR systems as represented by information about past search sessions. Specifically, agents maintain estimates on the downstream agents' abilities to provide relevant documents for incoming queries. These estimates are updated gradually by learning from the feedback information returned from previous search sessions. Based on this information, the agents derive corresponding routing policies. Thereafter, these agents route the queries based on the learned policies and update the estimates based on the new routing policies. Experimental results demonstrate that the learning algorithm improves considerably the routing performance on two test collection sets that have been used in a variety of distributed IR studies.

The information needs of search engine users vary in complexity, depending on the task they are trying to accomplish. Some simple needs can be satisfied with a single query, whereas others require a series of queries issued over a longer period of time. While search engines effectively satisfy many simple needs, searchers receive little support when their information needs span session boundaries. In this work, we propose methods for modeling and analyzing user search behavior that extends over multiple search sessions. We focus on two problems: (i) given a user query, identify all of the related queries from previous sessions that the same user has issued, and (ii) given a multi-query task for a user, predict whether the user will return to this task in the future. We model both problems within a classification framework that uses features of individual queries and long-term user search behavior at different granularity. Experimental evaluation of the proposed models for both tasks indicates that it is possible to effectively model and analyze cross-session search behavior. Our findings have implications for improving search for complex information needs and designing search engine features to support cross-session search tasks.

Session search is an Information Retrieval (IR) task which handles a series of queries issued for a search task. In this paper, we propose a novel reinforcement learning style information retrieval framework and develop a new feedback learning algorithm to model user feedback, including clicks and query reformulations, as reinforcement signals and to generate rewards in the RL framework. From a new perspective, we view session search as a cooperative game played between two agents, the user and the search engine. We study the communications between the two agents; they always exchange opinions on whether the current stage of search is relevant and whether we should explore now. The algorithm infers user feedback models by an EM algorithm from the query logs. We compare to several state-of-the-art session search algorithms and evaluate our algorithm on the most recent TREC 2012 to 2014 Session Tracks. The experimental results demonstrates that our approach is highly effective for improving session search accuracy.

Searching information retrieval systems is a highly interactive, iterative process that cannot be understood simply by comparing the output of a search session (the “search product”) to a query stated in advance. In this article, we examine evaluation goals and methods for studying information retrieval behavior, drawing examples from our own research and that of others. We limit our review to research that employs online monitoring, also known as transaction log analysis. Online monitoring is one of few methods that can capture detailed data on the search process at a reasonable cost; these data can be used to build quantitative models or to support qualitative interpretations of quantitative results. Monitoring is a data collection technique rather than a research design, and can be employed in experimental or field studies, whether alone or combined with other data collection methods. Based on the research questions of interest, the researcher must determine what variables to collect from each data source, which to treat as independent variables to manipulate, and which to treat as dependent variables to observe effects. Studies of searching behavior often treat search task and searcher characteristics as independent variables and may manipulate other independent variables specific to the research questions addressed. Search outcomes, time, and search paths frequently are treated as dependent variables. We discuss each of these sets of variables, illustrating them with sample results from the literature and from our own research. Our examples are drawn from the Science Library Catalog project, a 7-year study of children's searching behavior on an experimental retrieval system. We close with a brief discussion of the implications of these results for the design of information retrieval systems. © 1996 John Wiley & Sons, Inc.

The recent increase in the volume and variety of video content available online presents growing challenges for video search. Users face increased difficulty in formulating effective queries and search engines must deploy highly effective algorithms to provide relevant results. Although lately much effort has been invested in optimizing video search engine results, relatively little attention has been given to predicting for which queries results optimization is most useful, i.e., predicting which queries will fail. Being able to predict when a video search query would fail is likely to make the video search result optimization more efficient and effective, improve the search experience for the user by providing support in the query formulation process and in this way boost the development of video search engines in general. While insight about a query's performance in general could be obtained using the well-known concept of query performance prediction (QPP), we propose a novel approach for predicting a failure of a video search query in the specific context of a search session. Our 'context-aware query failure' prediction approach uses a combination of 'user indicators' and 'engine indicators' to predict whether a particular query is likely to fail in the context of a particular search session. User indicators are derived from the search log and capture the patterns of query (re)formulation behavior and the click-through data of a user during a typical video search session. Engine indicators are derived from the video search results list and capture the visual variance of search results that would be offered to the user for the given query. We validate our approach experimentally on a test set containing 1+ million video search queries and show its effectiveness compared to a set of conventional QPP baselines. Our approach achieves a 13% relative improvement over the baseline.

The means of retrieval of information owned by the library at Bung Hatta University is OPAC, a facility that implements an online system, this facility is one or several computers (PCs) provided by the library which can be used by all users to browse the library catalog which is already in the form of a base. electronic data. Online public access catalog (OPAC) basically provides more benefits in recovering information in the library. the use of information retrieval facilities by library users at Bung Hatta University by using needs, motives and interests. OPAC is known to some users at Bung Hatta Library from searching the internet and asking friends. The motive of students using OPAC is to get the collection of books they are looking for. on the indicator of interest in using OPAC facilities, namely to facilitate information retrieval.

Information retrieval (IR) systems aim to select relevant material from large collections of information in response to user queries. The approaches used to accomplish this have been the focus of much research and development over the past 50 years, and have led to the algorithms underlying many commercial and Web-based search engines today. This entry describes the common components that go into the design of IR systems (from text processing to inverted file indexes). The major classes (or models) of retrieval algorithms (Boolean, vector, and probabilistic) are described along with formal definitions of the basic form of these algorithms and some of the variations in common use in IR research. In addition, the entry examines query expansion techniques, and in particular relevance feedback, and how they are used in IR systems

Purpose – People often engage in different information-seeking strategies (ISSs) within a single information-seeking episode. A critical concern for the design of information retrieval (IR) systems is how to provide support for these different behaviors in a manner which searchers can easily understand, navigate and use, as they move from one ISS to another. The purpose of this paper is to describe a dialogue structure that was implemented in an experimental IR system, in order to address this concern. Design/methodology/approach – The authors conducted a user-centered experiment to evaluate the IR systems. Participants were asked to search for information on two different task types, with four different topics per task, in both the experimental system and a baseline system emulating state-of-the-art IR systems. The authors report here the results related explicitly to the use of the experimental system's dialogue structure. Findings – For one of the task types, most participants followed the search steps as predicted in the dialogue structures, and those who did so completed the task in fewer moves. For the other task type, predicted order of moves was often not followed, but participants again used fewer moves when following the predicted order. Results demonstrate that the dialogue structures the authors designed indeed support effective human information behavior patterns in a variety of ways, and that searchers can effectively use a system which changes to support different ISSs. Originality/value – This study shows that it is both possible and beneficial, to design an IR system which can support multiple ISSs, and that such a system can be understood and used successfully.

Personalization of search results offers the potential for significant improvement in information retrieval performance. User interactions with the system and documents during information-seeking sessions provide a wealth of information about user preferences and their task goals. In this paper, we propose methods for analyzing and modeling user search behavior in search sessions to predict document usefulness and then using information to personalize search results. We generate prediction models of document usefulness from behavior data collected in a controlled lab experiment with 32 participants, each completing uncontrolled searching for 4 tasks in the Web. The generated models are then tested with another data set of user search sessions in radically different search tasks and constrains. The documents predicted useful and not useful by the models are used to modify the queries in each search session using a standard relevance feedback technique. The results show that application of the models led to consistently improved performance over a baseline that did not take account of user interaction information. These findings have implications for designing systems for personalized search and improving user search experience.

We make use of search logs provided by the Belga News Agency to recommend images downloaded by previous users to new users. Each search session in the logs consists of a session ID number, the ID of the images which were downloaded at the conclusion of that session, and the various search terms which were input leading up to the selection and downloading of those images. In our approach, we match the queries of future users against the search terms in each session of the logs, and return the images selected in the best matching search sessions. In this way images considered relevant by previous users are recommended to future users with similar queries. An evaluation using P@50 for ten common queries produced encouraging results. This work describes a variation on the traditional Information Retrieval paradigm, where instead of text documents or images being indexed according to their content, they are indexed according to the search terms previous users have used in finding them.

Library online public access catalogues (OPACs) are automated information retrieval systems that constitute the most readily available source of information for non-professional users. Web-based OPACs represent a huge step forward in the development of user interfaces for catalogues. The present study evaluates Latin American OPACs against a checklist for full bibliographic display in web-based OPACs, developed at the University of Toronto. The checklist contains four sections that cover the most important facets of the OPAC: 1) labels, 2) text, 3) instructional information, and 4) page layout. Finally, multivariate analysis methods are applied to the results. Such techniques include 1) cluster analysis, 2) principal components analysis (PCA), and 3) multidimensional scaling (MDS).

Gathering information and making decisions based on retrieved information are the important tasks that every intelligence analyst is doing. User modeling techniques have been exploited to help analysts to search for information effectively. To justify the effects of any user modeling technique on helping analysts retrieve quality documents relevant to their tasks, we need to have a comprehensive evaluation method, which assesses the improvement of retrieval performance and user performance. In this paper, we describe our evaluation of a cognitive user model for information retrieval with regards to retrieval performance. Our user model captures user intent dynamically by analyzing behavioral information from retrieved relevant documents for improving the retrieval performance and user's performance. In this evaluation, we assess the user model's short-term effects on a single query, and the user model's long-term effects on the whole search session. We compare our approach with the best traditional approach for relevance feedback in information retrieval, the Ide dec-hi, which is the approach of modifying queries using term frequency from relevant/non-relevant documents. We use the oldest collection of information retrieval on aerodynamics called CRANFIELD. The results of this evaluation show that by exploring user intent, we achieve competitive performance in the feedback run compared to Ide dec-hi. At the same time, our user model approach offers the advantages of retrieving more quality documents at the initial run compared to the term frequency inverted document frequency (TFIDF) approach. Our results have shown that our user modeling approach can be used to improve efficiency, learnability and interactivity of an information retrieval system.

From highly relevant to not relevant: examining different regions of relevance

Integrated data base is a data base records bibliographic data of several types of document like textbook, periodicals, proceedings, research report, thesis, and article. Purposes of this study were to investigate document type description and searching strategy in integrated data base of online public access catalogs (OPAC) in PDII-LIPI, and to know user opinion about that data base existence. It used observation and interviews to 100 data base users to collect data. Result of this study stated that document type description in OPAC was not indexed, so users couldn't do information searching of special type of document directly in the data base. There was only one method could be used to search information of special type of document. User could select it from some information records on the computer screen as the result of document title, author name or subject searching in the data base. This information access method was not efficient. So document type description and indexing was an important factor and should be done in information retrieval system using integrated data base. But respondent majority (84%) in PDII-LIPI stated that they liked using separated data base to search information of special type of document.<br /><br />

This study seeks to understand the information-to-knowledge experience of English Language Learner (ELL) students in a Guided Inquiry project undertaken by the librarian and subject teachers. As a pilot study, it provides an in-depth examination of two Korean 11th grade students in a biology class of a high school in New Jersey, U.S. During the project, data were collected through questionnaire, surveys, search journals, search sessions, observation, students’ papers, and interviews. The findings of this study will facilitate the understanding on the information seeking and knowledge construction process of ELL students so that school environments, including school libraries, can provide meaningful instructional and service interventions for them.