• Journal of the Korean Society for Library and Information Science
• /
• v.35 no.3
• /
• pp.173-189
• /
• 2001

This paper presents a framework for observable behavior that can be used as a basis for user modeling, and it reports the results of a pair of user studies that examine the joint utility of two specific behaviors. User models can be constructed by hand, or they can be teamed automatically based on feedback provided by the user about the relevance of documents that they have examined. By observing user behavior, it is possible to obtain implicit feedback without requiring explicit relevance judgments. Four broad categories of potentially observable behavior are identified : examine, retain, reference, and annotate, and examples of specific behaviors within a category are further subdivided based on the natural scope of information objects being manipulated . segment object, or class. Previous studies using Internet discussion groups (USENET news) have shown reading time to be a useful source of implicit feedback for predicting a user's preferences. The experiments reported in this paper extend that work to academic and professional journal articles and abstracts, and explore the relationship between printing behavior and reading time. Two user studies were conducted in which undergraduate students examined articles or abstracts from the telecommunications or pharmaceutical literature. The results showed that reading time can be used to predict the user's assessment of relevance, that the mean reading time for journal articles and technical abstracts is longer than has been reported for USENET news documents, and that printing events provide additional useful evidence about relevance beyond that which can be inferred from reading time. The paper concludes with a brief discussion of the implications of the reported results.

• Journal of the Korean Society for Library and Information Science
• /
• v.52 no.4
• /
• pp.91-110
• /
• 2018

We proposed a video summarization algorithm based on an ERP (Event Related Potentials)-based topic relevance model, a MMR (Maximal Marginal Relevance), and discriminant analysis to generate a semantically meaningful video skim. We then conducted implicit and explicit evaluations to evaluate our proposed ERP/MMR-based method. The results showed that in the implicit and explicit evaluations, the average scores of the ERP / MMR methods were statistically higher than the average score of the SBD (Shot Boundary Detection) method used as a competitive baseline, respectively. However, there was no statistically significant difference between the average score of ERP/MMR (${\lambda}=0.6$) method and that of ERP/MMR (${\lambda}=1.0$) method in both assessments.

• Management & Information Systems Review
• /
• v.29 no.4
• /
• pp.23-44
• /
• 2010

This study examines the value-relevance of accruals and discretionary accruals. Also, by examining the effects of the corporate life-cycle on these relationship, this study is able to provide evidence of the value-relevance of accruals and discretionary accruals measures in the economic context of life-cycle theory. This study uses results based on life-cycle classification methods developed by Anthony and Ramesh(1992), adjust Jones model and Dechow Dechev(2002) model. We classify firms using individuals variables(sales growth, capital expenditure growth, employee growth) and then use a composite score obtained from all variables for classification. Our sample consists of 272 firms listed in the Korean Stock Exchange during 14 years(1996-2009). Our final sample for regression variables consists of 2,448 firm-year observations. This evidence implies that the value-relevance of accruals and discretionary accruals in the growth and mature stage can have positive impact on the price but in the decline the value-relevance of accruals and discretionary accruals can have negative impact on the price. The results mean that discretionary accruals communicate managements' private information in the growth stage, but. earnings management in the decline stage. The results of this study suggest that corporate life cycle stages influence the value-relevance of accruals and discretionary accruals measures.

• Journal of the Korean Society for Library and Information Science
• /
• v.16
• /
• pp.177-205
• /
• 1989

Retrieval effectiveness is the principal criteria for measuring the performance of an information retrieval system. This paper deals with the characteristics of 'relevance' of information and various measuring techniques of retrieval effectivess. The outlines of this study are as follows: 1) Relevance decision for evaluation should be devided into the user-oriented and the system-oriented decisions. 2) The recall-precision measure seems to be user-oriented, and the recall-fallout measure to be system-oriented. 3) Many of composite measures can not be justified III any rational manner unfortunately. 4) The Swets model has demonstrated that it yields, in general, a straight line instead of a curve of varying curvature and emphasized the fundamentally probabilistic nature of information retrieval. 5) The Cooper model seems to be a good substitute for precision and a useful measure for systems which ranked documents. 6) The Rocchio model were proposed for the evaluation of retreval systems which ranked documents, and were designed to be independent of cut-off. 7) The Cawkell model suggested that the Shannon's equation for entropy can be applied to measuring of retrieval effectiveness.

• Journal of Information Science Theory and Practice
• /
• v.9 no.2
• /
• pp.1-17
• /
• 2021

Pseudo relevance feedback (PRF) is a powerful query expansion (QE) technique that prepares queries using the top k pseudorelevant documents and choosing expansion elements. Traditional PRF frameworks have robustly handled vocabulary mismatch corresponding to user queries and pertinent documents; nevertheless, expansion elements are chosen, disregarding similarity to the original query's elements. Word embedding (WE) schemes comprise techniques of significant interest concerning QE, that falls within the information retrieval domain. Deep averaging networks (DANs) defines a framework relying on average word presence passed through multiple linear layers. The complete query is understandably represented using the average vector comprising the query terms. The vector may be employed for determining expansion elements pertinent to the entire query. In this study, we suggest a DANs-based technique that augments PRF frameworks by integrating WE similarities to facilitate Arabic information retrieval. The technique is based on the fundamental that the top pseudo-relevant document set is assessed to determine candidate element distribution and select expansion terms appropriately, considering their similarity to the average vector representing the initial query elements. The Word2Vec model is selected for executing the experiments on a standard Arabic TREC 2001/2002 set. The majority of the evaluations indicate that the PRF implementation in the present study offers a significant performance improvement compared to that of the baseline PRF frameworks.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.2 no.1
• /
• pp.9-14
• /
• 2002

Boolean retrieval is simple and elegant. However, since there is no provision for term weighting, no ranking of the answer set is generated. As a result, the size of the output might be too large or too small. Relevance feedback is the most popular query reformulation strategy. in a relevance feedback cycle, the user is presented with a list of the retrieved documents and, after examining them, marks those which are relevant. In practice, only the top 10(or 20) ranked documents need to be examined. The main idea consists of selecting important terms, or expressions, attached to the documents that have been identified as relevant by the user, and of enhancing the importance of these terms in a new query formulation. The expected effect is that the new query will be moved towards the relevant documents and away from the non-relevant ones. Local analysis techniques are interesting because they take advantage of the local context provided with the query. In this regard, they seem more appropriate than global analysis techniques. In a local strategy, the documents retrieved for a given query q are examined at query time to determine terms for query expansion. This is similar to a relevance feedback cycle but might be done without assistance from the user.

• Journal of Korea Multimedia Society
• /
• v.4 no.4
• /
• pp.287-296
• /
• 2001

This paper proposes an efficient algorithm to select the proper retrieval results from various information sources in Meta search. The algorithm collects and evaluates the related documents to the given query Then, it determines the appropriate retrieval results based on the relevance between the query and the collected documents. This algorithm depends on the Meta information such as the size N of population, top-ranked information of related documents and the precision in order to choose the most appropriate retrieval result.

• Journal of Information Technology Services
• /
• v.8 no.3
• /
• pp.67-81
• /
• 2009

This study aims to test the valur relevance of development cost particularly focusing on IT firms of KOSDAQ. Test period is from 2005 to 2007 and the samples are 2,271 year-firms including 1,692 firms that reported development cost in financial statements. The basic test model is a modified Ohlson(1995)'s linear model. The empirical results show that there is the negative relation between stock price and development cost reported as asset. It means that development costs reported as asset is considered as expense in the market. It implies that development activities of KOSDAQ IT firms is not related to market-leading technologies or goods. Otherwise it might reflect the conservative valuation of market on the unstability of KOSDAQ market itself.

• Journal of Korea Multimedia Society
• /
• v.24 no.8
• /
• pp.1122-1137
• /
• 2021

PM_2.5 concentration in Seoul could be predicted by deep neural network model. In this paper, the contribution of input factors to the model's prediction results is analyzed using the LRP(Layer-wise Relevance Propagation) technique. LRP analysis is performed by dividing the input data by time and PM concentration, respectively. As a result of the analysis by time, the contribution of the measurement factors is high in the forecast for the day, and those of the forecast factors are high in the forecast for the tomorrow and the day after tomorrow. In the case of the PM concentration analysis, the contribution of the weather factors is high in the low-concentration pattern, and that of the air quality factors is high in the high-concentration pattern. In addition, the date and the temperature factors contribute significantly regardless of time and concentration.

• Proceedings of the KSEEG Conference
• /
• 2002.04a
• /
• pp.412-414
• /
• 2002