Semantic Hilbert Space for Interactive Image Retrieval

This research is motivated by the lack of empirical studies that discuss the existence of libraries as a fulfillment of information needs, especially in the secretariat library of the West Sumatra provincial DPRD. The purpose of this study is to describe the extent of the relationship between the Existence of Libraries and the Information Needs of Users. This type of research is quantitative with a descriptive method. The population in this study were all registered users at the Secretariat Library of the West Sumatra Provincial DPRD. Samples were taken using simple random sampling, and data were collected through observation, interviews, and questionnaires. The results of the analysis showed that the existence of the library was in the good category with an average score of 3.01. The information needs of users were in the good category with an average score of 2.93. Based on the Pearson correlation test, it showed a positive relationship between the existence of the library and the information needs of users of 0.643, including in the interval scale of 0.60-0.799. This shows that the existence of the library has a significant relationship with the information needs of users. These results emphasize that the existence of libraries has a direct influence on the information needs of users, especially institutional libraries where users are members of the institution who have jobs in certain categories.

Personalized Web search is an effective means of providing specific results to different users when they submit the same query. How to obtain user's real-time information need is a key issue in personalized search. Existing methods concentrate more on the building user profile based on Web pages/documents which affects the efficiency of search engine. In addition, dynamics of user profile is often ignored. To address this problem, we introduce an approach that captures the user context to accurately provide preferences of users for effective personalized search in this paper. First, short-term query context is generated from Web-snippets to play a role of semantic background of user's search behavior, identifying related concepts of the query. Second, user context snap is built based on query context according to user's interactive search behavior. Finally, evolution of user context is considered by introducing forgetting factor to merge the independent user context snap in a user session. The experimental results fully demonstrate that our approach can successfully build user context according to individual user information need.

This minitrack addresses a broad range of issues related to the design of the next generation of information retrieval systems, especially related to improving web search effectiveness from the user perspective. The core of this research is how to help the user access and make use of digital data. These issues are of prime interest to researchers, developers, and information managers. The multidisciplinary nature of this emerging area allows us to engage as a community of participants with a wide range of interests, including retrieval design, HCI, information seeking behavior, and information management. Information Retrieval supports the computerized search of large document and digital media collections (millions or billions of items) to select small subsets of those documents relevant to a user's information need. Such algorithms are the basis for internet search engines and question-answering systems. At the same time, we know relatively little about the users who deal with a multi-billion-page Web. Further research is needed to address the user issues related to effectiveness and quality of experience when interacting with search engines. New metrics and methodologies are needed for retrieval evaluation as both the data sets of interest and user expectations grow exponentially. A focus on the user perspective allows us to align the user focus and the system focus in a multi-disciplinary forum that includes theoretical foundations, evaluation measures, methodologies, case studies and user study results. This year in the Information Access & Retrieval MiniTrack we include papers that cover this multidisciplinary approach. One paper, Appropriate Software Components for Mobile End-User Devices Executive Information Systems Up Close and Personalized as an Example, presents design principles for the design of interfaces for mobile devices. Two papers, Building Support for Web Information Gathering Tasks, and Characterizing Queries in Different Search Tasks, explore user behavior related to web search. The first paper examines characteristics of search specific to longer gathering tasks. The second of these papers explores the more general issue of types of tasks for a range of web information needs. The paper, Determinants of Information Channel Choice: The Impact of Task Complexity and Dispositional Character Traits, is a behavioral study of how made channel choice. The final paper, Cobre: A Comparative Book Reader for Los Primeros Libros, presents a novel reader model based on film strip metaphor. 2012 45th Hawaii International Conference on System Sciences

User studies in information science have recognised relevance as a multidimensional construct. An implication of multidimensional relevance is that a user's information need should be modeled by multiple data structures to represent different relevance dimensions. While the extant literature has attempted to model multiple dimensions of a user's information need, the fundamental assumption that a multidimensional model is better than a uni-dimensional model has not been addressed. This study seeks to test this assumption. Our results indicate that a retrieval system that models both topicality and the novelty dimension of a users' information need outperforms a system with a uni-dimensional model.

A set of words is often insufficient to express a user's information need. In order to account for various information needs associated with a query, diversification seems to be a reasonable strategy. By diversifying the result set, we increase the probability of results being relevant to the user's information needs when the given query is ambiguous. A diverse result set must contain a set of documents that cover various subtopics for a given query. We propose a graph based method which exploits the link structure of the web to return a ranked list that provides complete coverage for a query. Our method not only provides diversity to the results set, but also avoids excessive redundancy. Moreover, the probability of relevance of a document is conditioned on the documents that appear before it in the result list. We show the effectiveness of our method by comparing it with a query-likelihood model as the baseline.

In the past decade, the prosperity of the World Wide Web has led to fast explosion of information, and there is a long-standing demand on how to access such a huge volume of information effectively and efficiently. Information Retrieval (IR) aims to tackle the challenge by exploring approaches to obtain relevant information items (e.g., documents) relevant to a given information need (e.g., query) from a huge collection of textual data (e.g., the Web). Named entity (e.g., person, location, product, event, organization) is a type of term compound widely existing in textual data. Recent advances in Information Extraction make it possible to extract entities from large volume of free text efficiently, and the research community are actively exploring whether entities would contribute to the retrieval effectiveness. In this thesis, we investigate how to leverage entities to improve retrieval in several directions. We start with finding entities with certain semantic relation, which aims at retrieving entities and their associated attributes to meet user's information need directly. This is different from traditional search paradigm in which only documents are retrieved. Entity retrieval is performed by first retrieving a list of documents and then extracting entities from those documents. We propose a novel probabilistic framework which leverages supporting documents as bridge to model the relevance between query and entities and rank entities accordingly. On the other side, we also explore how to leverage entities to improve effectiveness of ad hoc document retrieval in two directions. The first direction is entity-centric query expansion. We find related entities of query, and perform query expansion using the names and relations of related entities. Significant improvements over several state-of-the-art feedback models could be observed on multiple data collections. Besides, we explore another direction: entity-centric relevance modeling. We propose a novel retrieval approach, i.e., Latent Entity Space (LES), which models the relevance by leveraging entity profiles to represent semantic content of documents and queries. Experimental results over several TREC collections show that LES is effective on capturing latent semantic content and can significantly improve the search accuracy of several state-of-the-art retrieval models for entity-bearing queries. This thesis presents a series of research efforts on entity centric information retrieval in several directions, and reveals promising potential of entities on improving the retrieval effectiveness. With the fast curation of high-quality knowledge base, more information about entities could be easily accessed and integrated into retrieval models. We hope our work could serve as guideline for future work on leveraging entities to improve information retrieval in more applications.

The explosive growth of information on the web demands effective intelligent search and filtering methods. Consequently, techniques have been developed that extract conceptual information from the document and use the conceptual information in the user profile to form part of the user's information intent from his/her query. In a similar vein, we build the profile without user interaction, automatically monitoring the user's browsing habits. These profiles, in turn, are used to automatically learn the semantic context of user's information need. These sets of categories can serve as a context to disambiguate the words in the user's query. In this paper, we present a framework for assisting the user in one of the most difficult information retrieval tasks, i.e., that of formulating an effective search query. Our experimental results show that implicit measurements of user interests, combined with the semantic knowledge embedded in a concept hierarchy, can be used effectively to infer the user context and to improve the results of information retrieval.

Relevance is the central concept of information retrieval. Although its important role is unanimously accepted among researchers, numerous different definitions of the term have emerged over the years. Considerable effort has been put into creating consistent and universally applicable descriptions of relevance in the form of relevance frameworks. Across these various formal systems of relevance, a wide range of relevance criteria has been identified. The probably most frequently used single criterion, that in some applications even becomes a synonym for relevance, is topicality. It expresses a document's topical overlap with the user's information need. For textual resources, it is often estimated based on term co-occurrences between query and document. There is, however, a significant number of further noteworthy relevance criteria. Prominent specimen are: (Currency) determines how recent and up to date the document is. Outdated information may have become invalid over time.(Availability) expresses how easy it is to obtain the document. Users might not want to invest more than a threshold amount of resources (e.g., disk space, downloading time or money) to get the document.(Readability) describes the document's readability and understandability. A document with a high topical relevance towards a given information need can become irrelevant if the user is not able to extract the desired information from it.(Credibility) contains criteria such as the document author's expertise, the publication's reputation and the document's general trustworthiness.(Novelty) describes the document's contribution to satisfying an information need with respect to the user's context. E.g., previous search results or general knowledge about the domain.It is evident that these criteria can have very different scopes. Some of them are static characteristics of the document or the author, others depend on the concrete information need at hand or even the user's search context. Currently, state-of-the-art retrieval models often treat relevance (regardless which interpretation of the term was chosen) as an atomic concept that can be expressed through topical overlap between document and query or a plain linear combination of multiple scores. Considering the broad audiences a web search engine has to serve, such a method does not seem optimal as the concrete composition of relevance will vary from person to person depending on social and educational context. Furthermore, each individual can be expected to have situational preference for certain combinations of relevance facets depending on the information need at hand. We investigate combination schemes which respect the dimension-specific relevance distributions. In particular, we developed a risk-aware method of combining relevance criteria inspired by the economic Portfolio theory. As a first stage, we applied this method for result set diversification across dimensions.

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

There is a growing need to access and retrieve relevant information across media, across languages and across modalities. The research context concerning information access and retrieval aims at modelling, designing and implementing systems able to provide a fast and effective content-based access to a large amount of multimedia information. The aim of such systems is to estimate the relevance of documents to a user information need. This is a very hard and complex task due to several distinct reasons that a large volume of research has attempted to analyse and tackle. Information Retrieval (IR) can be considered as the first historical research area aimed at defining systems for the automatic access to huge amounts of information (together with DBMSs whose main aim is to manage and access huge amounts of data).

As the Web has become one of the biggest repository of knowledge easily accessible for everyone, information retrieval has stopped to be an exclusive field of information sciences specialists, and it has become a field related with any person. Although users do not have to know information retrieval techniques, the proposal of this work is to improve query results by using a that implements these techniques. For this, a semantic refiner is proposed. This refiner acts as a specialist in information sciences and prepares, using linguistic resources, an appropriate search strategy that represents the user's information need. The semantic refinement consists on three steps. First, it guides the user for sense disambiguation of the words submitted by him. Then, it allows the user to select concepts hierarchically related to reduce the amount of documents to retrieve. Finally, it expands semantically concepts to increase the amount of documents to be retrieved. A prototype is implemented using WordNet as linguistic resource, and experimental results are analyzed.

As a method for information retrieval (IR) on the Web, search engines have become the tool of choice for most online users. However, despite the variety of next generation approaches to Web search we have seen recently (e.g. [1,2]), the problems of information overload, vague user queries and spam still have the effect that many search sessions end in user frustration. Generally search engines are criticised for returning result lists that have low precision, where the user's information need is not satisfied by any of the returned result pages.

The objective of the paper is to amalgamate theories of text retrieval from various research traditions into a cognitive theory for information retrieval interaction. Set in a cognitive framework, the paper outlines the concept of polyrepresentation applied to both the user's cognitive space and the information space of IR systems. The concept seeks to represent the current user's information need, problem state, and domain work task or interest in a structure of causality. Further, it implies that we should apply different methods of representation and a variety of IR techniques of different cognitive and functional origin simultaneously to each semantic full‐text entity in the information space. The cognitive differences imply that by applying cognitive overlaps of information objects, originating from different interpretations of such objects through time and by type, the degree of uncertainty inherent in IR is decreased. Polyrepresentation and the use of cognitive overlaps are associated with, but not identical to, data fusion in IR. By explicitly incorporating all the cognitive structures participating in the interactive communication processes during IR, the cognitive theory provides a comprehensive view of these processes. It encompasses the ad hoc theories of text retrieval and IR techniques hitherto developed in mainstream retrieval research. It has elements in common with van Rijsbergen and Lalmas' logical uncertainty theory and may be regarded as compatible with that conception of IR. Epistemologically speaking, the theory views IR interaction as processes of cognition, potentially occurring in all the information processing components of IR, that may be applied, in particular, to the user in a situational context. The theory draws upon basic empirical results from information seeking investigations in the operational online environment, and from mainstream IR research on partial matching techniques and relevance feedback. By viewing users, source systems, intermediary mechanisms and information in a global context, the cognitive perspective attempts a comprehensive understanding of essential IR phenomena and concepts, such as the nature of information needs, cognitive inconsistency and retrieval overlaps, logical uncertainty, the concept of ‘document’, relevance measures and experimental settings. An inescapable consequence of this approach is to rely more on sociological and psychological investigative methods when evaluating systems and to view relevance in IR as situational, relative, partial, differentiated and non‐linear. The lack of consistency among authors, indexers, evaluators or users is of an identical cognitive nature. It is unavoidable, and indeed favourable to IR. In particular, for full‐text retrieval, alternative semantic entities, including Salton et al.'s ‘passage retrieval’, are proposed to replace the traditional document record as the basic retrieval entity. These empirically observed phenomena of inconsistency and of semantic entities and values associated with data interpretation support strongly a cognitive approach to IR and the logical use of polyrepresentation, cognitive overlaps, and both data fusion and data diffusion.

We consider a search task as a set of queries that serve the same user information need. Analyzing search tasks from user query streams plays an important role in building a set of modern tools to improve search engine performance. In this paper, we propose a probabilistic method for identifying and labeling search tasks based on the following intuitive observations: queries that are issued temporally close by users in many sequences of queries are likely to belong to the same search task, meanwhile, different users having the same information needs tend to submit topically coherent search queries. To capture the above intuitions, we directly model query temporal patterns using a special class of point processes called Hawkes processes, and combine topic models with Hawkes processes for simultaneously identifying and labeling search tasks. Essentially, Hawkes processes utilize their self-exciting properties to identify search tasks if influence exists among a sequence of queries for individual users, while the topic model exploits query co-occurrence across different users to discover the latent information needed for labeling search tasks. More importantly, there is mutual reinforcement between Hawkes processes and the topic model in the unified model that enhances the performance of both. We evaluate our method based on both synthetic data and real-world query log data. In addition, we also apply our model to query clustering and search task identification. By comparing with state-of-the-art methods, the results demonstrate that the improvement in our proposed approach is consistent and promising.

Web Search has seen two big changes recently: rapid growth in mobile search traffic, and an increasing trend towards providing answer-like results for relatively simple information needs (e.g., [weather today]). Such results display the answer or relevant information on the search page itself without requiring a user to click. While clicks on organic search results have been used extensively to infer result relevance and search satisfaction, clicks on answer-like results are often rare (or meaningless), making it challenging to evaluate answer quality. Together, these call for better measurement and understanding of search satisfaction on mobile devices. In this paper, we studied whether tracking the browser viewport (visible portion of a web page) on mobile phones could enable accurate measurement of user attention at scale, and provide good measurement of search satisfaction in the absence of clicks. Focusing on answer-like results in web search, we designed a lab study to systematically vary answer presence and relevance (to the user's information need), obtained satisfaction ratings from users, and simultaneously recorded eye gaze and viewport data as users performed search tasks. Using this ground truth, we identified increased scrolling past answer and increased time below answer as clear, measurable signals of user dissatisfaction with answers. While the viewport may contain three to four results at any given time, we found strong correlations between gaze duration and viewport duration on a per result basis, and that the average user attention is focused on the top half of the phone screen, suggesting that we may be able to scalably and reliably identify which specific result the user is looking at, from viewport data alone.