Interactive Intent Modeling for Exploratory Search

Exploratory search requires the system to assist the user in comprehending the information space and expressing evolving search intents for iterative exploration and retrieval of information. We introduce interactive intent modeling, a technique that models a user’s evolving search intents and visualizes them as keywords for interaction. The user can provide feedback on the keywords, from which the system learns and visualizes an improved intent estimate and retrieves information. We report experiments comparing variants of a system implementing interactive intent modeling to a control system. Data comprising search logs, interaction logs, essay answers, and questionnaires indicate significant improvements in task performance, information retrieval performance over the session, information comprehension performance, and user experience. The improvements in retrieval effectiveness can be attributed to the intent modeling and the effect on users’ task performance, breadth of information comprehension, and user experience are shown to be dependent on a richer visualization. Our results demonstrate the utility of combining interactive modeling of search intentions with interactive visualization of the models that can benefit both directing the exploratory search process and making sense of the information space. Our findings can help design personalized systems that support exploratory information seeking and discovery of novel information.Peer reviewe

Exploratory Search Using Interactive Visualization Techniques

This thesis studies exploratory search of large datasets using machine learning and human computer interaction techniques. It is often that the user wants to search for something but cannot formulate the exact query or the keywords which would help him to reach the most relevant search results. One might also want to address these issues about a particular topic by gathering information after each search. We worked on extending an existing interactive exploratory search system known as SciNet. SciNet allows the users to provide relevant feedback to the system using interactive user interface. The system allows the users to direct their search query using interactive intent modeling. The users obtain relevant results by giving personalized feedback to the system through a radar based layout. Our aim is to make SciNet work for a large news data set and add new features which allow the users to explore and investigate the news articles. We try to visualize the entire collection of news articles stored in the system using neighborhood embedding and display it as an interactive map to the user. The locations of the search results are displayed on the map using markers. The users can explore the articles by clicking on markers. They can also select areas of the map where search results are located, which would enable them to view a list of most relevant unigrams in an area and are able to select relevant unigrams to boost the query. This serves as an additional feedback mechanism. We performed user experiments with twenty users to compare the performances of the original SciNet and the new extended system. The user experiments clearly showed that the extended system performs better than the original one. We also took feedback from the participants of the experiments in a form of a questionnaire, which showed that the extended system improves the overall user experience. We can further improve the performance of the new system by adding more features like tagging different regions of the map with descriptive keywords and using distributed computing based algorithms which would allows us to incorporate more data from different domains

Re-examining and re-conceptualising enterprise search and discovery capability: towards a model for the factors and generative mechanisms for search task outcomes.

Many organizations are trying to re-create the Google experience, to find and exploit their own corporate information. However, there is evidence that finding information in the workplace using search engine technology has remained difficult, with socio-technical elements largely neglected in the literature. Explication of the factors and generative mechanisms (ultimate causes) to effective search task outcomes (user satisfaction, search task performance and serendipitous encountering) may provide a first step in making improvements. A transdisciplinary (holistic) lens was applied to Enterprise Search and Discovery capability, combining critical realism and activity theory with complexity theories to one of the worlds largest corporations. Data collection included an in-situ exploratory search experiment with 26 participants, focus groups with 53 participants and interviews with 87 business professionals. Thousands of user feedback comments and search transactions were analysed. Transferability of findings was assessed through interviews with eight industry informants and ten organizations from a range of industries. A wide range of informational needs were identified for search filters, including a need to be intrigued. Search term word co-occurrence algorithms facilitated serendipity to a greater extent than existing methods deployed in the organization surveyed. No association was found between user satisfaction (or self assessed search expertise) with search task performance and overall performance was poor, although most participants had been satisfied with their performance. Eighteen factors were identified that influence search task outcomes ranging from user and task factors, informational and technological artefacts, through to a wide range of organizational norms. Modality Theory (Cybersearch culture, Simplicity and Loss Aversion bias) was developed to explain the study observations. This proposes that at all organizational levels there are tendencies for reductionist (unimodal) mind-sets towards search capability leading to fixes that fail. The factors and mechanisms were identified in other industry organizations suggesting some theory generalizability. This is the first socio-technical analysis of Enterprise Search and Discovery capability. The findings challenge existing orthodoxy, such as the criticality of search literacy (agency) which has been neglected in the practitioner literature in favour of structure. The resulting multifactorial causal model and strategic framework for improvement present opportunities to update existing academic models in the IR, LIS and IS literature, such as the DeLone and McLean model for information system success. There are encouraging signs that Modality Theory may enable a reconfiguration of organizational mind-sets that could transform search task outcomes and ultimately business performance

Learning to Reduce Annotation Load

Modern machine learning methods and their applications in computer vision are known to crave for large amounts of training data to reach their full potential. Because training data is mostly obtained through humans who manually label samples, it induces a significant cost. Therefore, the problem of reducing the annotation load is of great importance for the success of machine learning methods. We study the problem of reducing the annotation load from two viewpoints, by answering the questions âWhat to annotate?â and âHow to annotate?â. The question âWhat?â addresses the selection of a small portion of the data that would be sufficient to train an accurate model. The question âHow? focuses on minimising the effort of labelling each datapoint. The question âWhat to annotate?â becomes particularly compelling if we can select data to be annotated in an iterative and adaptive way, a setting known as active learning (AL). The key challenge in AL is to identify the datapoints that are the most informative for the model at a given stage. We propose several techniques to address this challenge. Firstly, we consider the problem of segmenting natural images and image volumes. We take advantage of image priors, such as smoothness of objects of interest, and use them in a novel form of geometric uncertainty. Using this, we design an AL technique to efficiently annotate data that is tailored to segmentation applications. Next, we notice that no single manually-designed strategy outperforms others in every application and that often the burden of designing new strategies outweighs the benefits of AL. To overcome this problem we suggest learning an AL strategy from data by formulating the AL problem as a regression task that predicts the reduction in the generalisation error achieved by labelling each datapoint. This enables us to learn AL strategies from simulated data and to transfer them to new datasets. Finally, we turn towards non-myopic data-driven AL strategies. To this end, we formulate the AL problem as a Markov decision process and find the best selection policy using reinforcement learning. We design the decision process such that the policy can be learnt for any ML model and transferred to diverse application domains. Effectively addressing the question âHow to annotate?â is of no less importance as large cost savings can be achieved by labelling each datapoint more efficiently. This can be done with intelligent interfaces that interact with a human annotator. We make two contributions towards answering the question âHow?â. Firstly, we propose an efficient technique to annotate 3D image volumes for image segmentation. Annotating data in 3D is cumbersome and an obvious way to facilitate it is to select a subset of the data lying on a 2D plane. To find the optimal plane (i.e. the one containing the most informative datapoints) we design a branch-and-bound algorithm that quickly eliminates hypotheses about the optimal projection. Secondly, we propose an intelligent data annotation method to train object detectors. Instead of always asking the human annotator to draw bounding boxes in images, we detect automatically in which cases we can rely on the current detector and verify its proposal

Supporting Exploratory Search Tasks Through Alternative Representations of Information

Information seeking is a fundamental component of many of the complex tasks presented to us, and is often conducted through interactions with automated search systems such as Web search engines. Indeed, the ubiquity of Web search engines makes information so readily available that people now often turn to the Web for all manners of information seeking needs. Furthermore, as the range of online information seeking tasks grows, more complex and open-ended search activities have been identified. One type of complex search activities that is of increasing interest to researchers is exploratory search, where the goal involves "learning" or "investigating", rather than simply "looking-up". Given the massive increase in information availability and the use of online search for tasks beyond simply looking-up, researchers have noted that it becomes increasingly challenging for users to effectively leverage the available online information for complex and open-ended search activities. One of the main limitations of the current document retrieval paradigm offered by modern search engines is that it provides a ranked list of documents as a response to the searcher’s query with no further support for locating and synthesizing relevant information. Therefore, the searcher is left to find and make sense of useful information in a massive information space that lacks any overview or conceptual organization. This thesis explores the impact of alternative representations of search results on user behaviors and outcomes during exploratory search tasks. Our inquiry is inspired by the premise that exploratory search tasks require sensemaking, and that sensemaking involves constructing and interacting with representations of knowledge. As such, in order to provide the searchers with more support in performing exploratory activities, there is a need to move beyond the current document retrieval paradigm by extending the support for locating and externalizing semantic information from textual documents and by providing richer representations of the extracted information coupled with mechanisms for accessing and interacting with the information in ways that support exploration and sensemaking. This dissertation presents a series of discrete research endeavour to explore different aspects of providing information and presenting this information in ways that both extraction and assimilation of relevant information is supported. We first address the problem of extracting information – that is more granular than documents – as a response to a user's query by developing a novel information extraction system to represent documents as a series of entity-relationship tuples. Next, through a series of designing and evaluating alternative representations of search results, we examine how this extracted information can be represented such that it extends the document-based search framework's support for exploratory search tasks. Finally, we assess the ecological validity of this research by exploring error-prone representations of search results and how they impact a searcher's ability to leverage our representations to perform exploratory search tasks. Overall, this research contributes towards designing future search systems by providing insights into the efficacy of alternative representations of search results for supporting exploratory search activities, culminating in a novel hybrid representation called Hierarchical Knowledge Graphs (HKG). To this end we propose and develop a framework that enables a reliable investigation of the impact of different representations and how they are perceived and utilized by information seekers

Visualisation des résultats de recherche classifiés en contexte de recherche d’information exploratoire : une évaluation d’utilisabilité

La recherche d’information exploratoire sur le Web présente des défis cognitifs en termes de stratégies cognitives et de tactiques de recherche. Le modèle « question-réponse » des moteurs de recherche actuels est inadéquat pour faciliter les stratégies de recherche d’information exploratoire, assimilables aux stratégies cognitives de l’apprentissage. La visualisation des résultats de recherche est un dispositif qui possède des propriétés graphiques et interactives pertinentes pour le traitement de l’information et l’utilisation de la mémoire et, plus largement de la cognition humaine. Plusieurs recherches ont été menées dans ce contexte de recherche d’information exploratoire, mais aucune n’a distinctement isolé le facteur graphique et interactif de la « visualisation » au sein de son évaluation. L’objectif principal de cette thèse est de vérifier si la visualisation des résultats en contexte de recherche d’information exploratoire témoigne des avantages cognitifs et interactifs pressentis selon ses présupposés théoriques. Pour décrire et déterminer la valeur ajoutée de la visualisation des résultats de recherche dans un contexte de recherche d’information exploratoire sur le Web, cette recherche propose de mesurer son utilisabilité. En la comparant selon les mêmes critères et indicateurs à une interface homologue textuelle, nous postulons que l’interface visuelle atteindra une efficacité, efficience et satisfaction supérieure à l’interface textuelle, dans un contexte de recherche d’information exploratoire. Les mesures objectives de l’efficacité et de l’efficience reposent principalement sur l’analyse des traces de l’interaction des utilisateurs, leur nombre et leur durée. Les mesures subjectives attestant de la satisfaction procurée par l’usage du système dans ce contexte repose sur la perception des utilisateurs par rapport à des critères de perception de la facilité d’utilisation et de l’utilité de l’interface testée et par rapport à des questions plus large sur l’expérience de recherche vécue. Un questionnaire et un entretien ont été passés auprès de chacun des vingt-trois répondants. Leur session de recherche a aussi été enregistré par un logiciel de capture vidéo d’écran. Sur les données des vingt-trois utilisateurs divisés en deux groupes, l’analyse statistique a révélé de faibles différences significatives entre les deux interfaces. Selon les mesures effectuées, l’interface textuelle s’est révélée plus efficace en terme de rappel et de pertinence ; et plus efficiente pour les durées de la recherche d’information. Sur le plan de la satisfaction, les interfaces ont été appréciées toutes deux posivitivement, ne permettant pas de les distinguer pour la grande majorité des métriques. Par contre, au niveau du comportement interactif, des différences notables ont montré que les utilisateurs de l’interface visuelle ont réalisé davantage d’interactions de type exploratoire, et ont procédé à une collecte sélective des résultats de recherche. L’analyse statistique et de contenu sur le critère de l’expérience vécue a permis de démontrer que la visualisation offre l’occasion à l’utilisateur de s’engager davantage dans le processus de recherche d’information en raison de l’impact positif de l’esthétique de l’interface visuelle. De plus, la fonctionnalité de classification a été perçue de manière ambivalente, divisant les candidats peu importe l’interface testée. Enfin, l’analyse des verbatims des « visuelle » a permis d’identifier le besoin de fonctionnalités de rétroaction de l’utilisateur afin de pouvoir communiquer le besoin d’information ou sa pondération des résultats ou des classes, grâce à des modalités interactives de manipulation directe des classes sur un espace graphique.Conducting exploratory searches on the web presents a number of cognitive difficulties as regards search strategies and tactics. The “question-response” model used by the available search engines does not respond adequately to exploratory searches, which are akin to cognitive learning strategies. Visualising search results involves graphic and interactive properties for presenting information that are pertinent for processing and using information, as well as for remembering and, more broadly, for human cognition. Many studies have been conducted in the area of exploratory searches, but none have focussed specifically on the graphic and interactive features of visualisation in their analysis. The principal objective of this thesis is to confirm whether the visualisation of results in the context of exploratory searches offers the cognitive and interactive advantages predicted by conjectural theory. In order to describe and to determine the added value of visualising search results in the context of exploratory web searches, the study proposes to measure its usability. By comparing it to a parallel text interface, using the same criteria and indicators, the likelihood of better efficiency, efficacy, and satisfaction when using a visual interface can be established. The objective measures of efficiency and efficacy are based mainly on the analysis of user interactions, including the number of these interactions and the time they take. Subjective measures of satisfaction in using the system in this context are based on user perception regarding ease of use and the usefulness of the interface tested, and on broader questions concerning the experience of using the search interface. These data were obtained using a questionnaire and a discussion with each participant. Statistical analysis of the data from twenty-three participants divided into two groups showed slightly significant differences between the two interfaces. Analysis of the metrics used showed that the textual interface is more efficient in terms of recall and pertinence, and more efficacious concerning the time needed to search for information. Regarding user satisfaction, both interfaces were seen positively, so that no differences emerged for the great majority of metrics used. However, as regards interactive behaviour, notable differences emerged. Participants using the visual interface had more exploratory interaction, and went on to select and collect pertinent search results. Statistical and content analysis of the experience itself showed that visualisation invites the user to become more involved in the search process, because of the positive effect of a pleasing visual interface. In addition, the classification function was perceived as ambivalent, dividing the participants no matter which interface was used. Finally, analysis of the verbatim reports of participants classed as “visual” indicated the need for a user feedback mechanism in order to communicate information needs or for weighting results or classes, using the interactive function for manipulating classes within a geographic space